Merge pull request #850 from cuicheng01/develop_reg
Update some configs and get_start docspull/851/head
cuicheng01
GitHub
commit
fd4a548897
|
|
@ -22,13 +22,13 @@ PaddleClas目前支持的训练/评估环境如下：
|
|||
准备好配置文件之后，可以使用下面的方式启动训练。
|
||||
|
||||
```
|
||||
python tools/train.py \
|
||||
-c configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml \
|
||||
-o pretrained_model="" \
|
||||
-o use_gpu=True
|
||||
python3 tools/train.py \
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Arch.pretrained=False \
|
||||
-o Global.device=gpu
|
||||
```
|
||||
|
||||
其中，`-c`用于指定配置文件的路径，`-o`用于指定需要修改或者添加的参数，其中`-o pretrained_model=""`表示不使用预训练模型，`-o use_gpu=True`表示使用GPU进行训练。如果希望使用CPU进行训练，则需要将`use_gpu`设置为`False`。
|
||||
其中，`-c`用于指定配置文件的路径，`-o`用于指定需要修改或者添加的参数，其中`-o Arch.pretrained=False`表示不使用预训练模型，`-o Global.device=gpu`表示使用GPU进行训练。如果希望使用CPU进行训练，则需要将`Global.device`设置为`cpu`。
|
||||
|
||||
更详细的训练配置，也可以直接修改模型对应的配置文件。具体配置参数参考[配置文档](config.md)。
|
||||
|
||||
|
|
@ -37,9 +37,9 @@ python tools/train.py \
|
|||
* 如果在训练中使用了mixup或者cutmix的数据增广方式，那么日志中将不会打印top-1与top-k（默认为5）信息：
|
||||
```
|
||||
...
|
||||
epoch:0 , train step:20 , loss: 4.53660, lr: 0.003750, batch_cost: 1.23101 s, reader_cost: 0.74311 s, ips: 25.99489 images/sec, eta: 0:12:43
|
||||
[Train][Epoch 3/20][Avg]CELoss: 6.46287, loss: 6.46287
|
||||
...
|
||||
END epoch:1 valid top1: 0.01569, top5: 0.06863, loss: 4.61747, batch_cost: 0.26155 s, reader_cost: 0.16952 s, batch_cost_sum: 10.72348 s, ips: 76.46772 images/sec.
|
||||
[Eval][Epoch 3][Avg]CELoss: 5.94309, loss: 5.94309, top1: 0.01961, top5: 0.07941
|
||||
...
|
||||
```
|
||||
|
||||
|
|
@ -47,9 +47,9 @@ python tools/train.py \
|
|||
|
||||
```
|
||||
...
|
||||
epoch:0 , train step:30 , top1: 0.06250, top5: 0.09375, loss: 4.62766, lr: 0.003728, batch_cost: 0.64089 s, reader_cost: 0.18857 s, ips: 49.93080 images/sec, eta: 0:06:18
|
||||
[Train][Epoch 3/20][Avg]CELoss: 6.12570, loss: 6.12570, top1: 0.01765, top5: 0.06961
|
||||
...
|
||||
END epoch:0 train top1: 0.01310, top5: 0.04738, loss: 4.65124, batch_cost: 0.64089 s, reader_cost: 0.18857 s, batch_cost_sum: 13.45863 s, ips: 49.93080 images/sec.
|
||||
[Eval][Epoch 3][Avg]CELoss: 5.40727, loss: 5.40727, top1: 0.07549, top5: 0.20980
|
||||
...
|
||||
```
|
||||
|
||||
|
|
@ -60,13 +60,13 @@ python tools/train.py \
|
|||
根据自己的数据集路径设置好配置文件后，可以通过加载预训练模型的方式进行微调，如下所示。
|
||||
|
||||
```
|
||||
python tools/train.py \
|
||||
-c configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml \
|
||||
-o pretrained_model="./pretrained/MobileNetV3_large_x1_0_pretrained" \
|
||||
-o use_gpu=True
|
||||
python3 tools/train.py \
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Arch.pretrained=True \
|
||||
-o Global.device=gpu
|
||||
```
|
||||
|
||||
其中`-o pretrained_model`用于设置加载预训练模型权重文件的地址，使用时需要换成自己的预训练模型权重文件的路径，也可以直接在配置文件中修改该路径。
|
||||
其中`Arch.pretrained`设置为`True`表示加载ImageNet的预训练模型，此外，`Arch.pretrained`也可以指定具体的模型权重文件的地址，使用时需要换成自己的预训练模型权重文件的路径。
|
||||
|
||||
我们也提供了大量基于`ImageNet-1k`数据集的预训练模型，模型列表及下载地址详见[模型库概览](../models/models_intro.md)。
|
||||
|
||||
|
|
@ -76,28 +76,29 @@ python tools/train.py \
|
|||
如果训练任务因为其他原因被终止，也可以加载断点权重文件，继续训练：
|
||||
|
||||
```
|
||||
python tools/train.py \
|
||||
-c configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml \
|
||||
-o checkpoints="./output/MobileNetV3_large_x1_0/5/ppcls" \
|
||||
-o last_epoch=5 \
|
||||
-o use_gpu=True
|
||||
python3 tools/train.py \
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Global.checkpoints="./output/MobileNetV3_large_x1_0/epoch_5" \
|
||||
-o Optimizer.lr.last_epoch=5 \
|
||||
-o Global.device=gpu
|
||||
```
|
||||
|
||||
其中配置文件不需要做任何修改，只需要在继续训练时设置`checkpoints`参数即可，表示加载的断点权重文件路径，使用该参数会同时加载保存的断点权重和学习率、优化器等信息。
|
||||
|
||||
**注意**：
|
||||
* 参数`-o last_epoch=5`表示将上一次训练轮次数记为`5`，即本次训练轮次数从`6`开始计算，该值默认为-1，表示本次训练轮次数从`0`开始计算。
|
||||
* 参数`-o Optimizer.lr.last_epoch=5`表示将上一次训练轮次数记为`5`，即本次训练轮次数从`6`开始计算，该值默认为-1，表示本次训练轮次数从`0`开始计算。
|
||||
|
||||
* `-o Global.checkpoints`参数无需包含断点权重文件的后缀名，上述训练命令会在训练过程中生成如下所示的断点权重文件，若想从断点`5`继续训练，则`Global.checkpoints`参数只需设置为`"../output/MobileNetV3_large_x1_0/epoch_5"`，PaddleClas会自动补充后缀名。
|
||||
|
||||
* `-o checkpoints`参数无需包含断点权重文件的后缀名，上述训练命令会在训练过程中生成如下所示的断点权重文件，若想从断点`5`继续训练，则`checkpoints`参数只需设置为`"./output/MobileNetV3_large_x1_0_gpupaddle/5/ppcls"`，PaddleClas会自动补充后缀名。
|
||||
```shell
|
||||
output/
|
||||
└── MobileNetV3_large_x1_0
|
||||
├── 0
|
||||
│ ├── ppcls.pdopt
|
||||
│ └── ppcls.pdparams
|
||||
├── 1
|
||||
│ ├── ppcls.pdopt
|
||||
│ └── ppcls.pdparams
|
||||
output
|
||||
├── MobileNetV3_large_x1_0
|
||||
│ ├── best_model.pdopt
|
||||
│ ├── best_model.pdparams
|
||||
│ ├── best_model.pdstates
|
||||
│ ├── epoch_1.pdopt
|
||||
│ ├── epoch_1.pdparams
|
||||
│ ├── epoch_1.pdstates
|
||||
.
|
||||
.
|
||||
.
|
||||
|
|
@ -109,20 +110,18 @@ python tools/train.py \
|
|||
可以通过以下命令进行模型评估。
|
||||
|
||||
```bash
|
||||
python tools/eval.py \
|
||||
-c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml \
|
||||
-o pretrained_model="./output/MobileNetV3_large_x1_0/best_model/ppcls"\
|
||||
-o load_static_weights=False
|
||||
python3 tools/eval.py \
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Global.pretrained_model=./output/MobileNetV3_large_x1_0/best_model
|
||||
```
|
||||
|
||||
上述命令将使用`./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml`作为配置文件，对上述训练得到的模型`./output/MobileNetV3_large_x1_0/best_model/ppcls`进行评估。你也可以通过更改配置文件中的参数来设置评估，也可以通过`-o`参数更新配置，如上所示。
|
||||
上述命令将使用`./configs/quick_start/MobileNetV3_large_x1_0.yaml`作为配置文件，对上述训练得到的模型`./output/MobileNetV3_large_x1_0/best_model`进行评估。你也可以通过更改配置文件中的参数来设置评估，也可以通过`-o`参数更新配置，如上所示。
|
||||
|
||||
可配置的部分评估参数说明如下：
|
||||
* `ARCHITECTURE.name`：模型名称
|
||||
* `pretrained_model`：待评估的模型文件路径
|
||||
* `load_static_weights`：待评估模型是否为静态图模型
|
||||
* `Arch.name`：模型名称
|
||||
* `Global.pretrained_model`：待评估的模型文件路径
|
||||
|
||||
**注意：** 如果模型为动态图模型，则在加载待评估模型时，需要指定模型文件的路径，但无需包含文件后缀名，PaddleClas会自动补齐`.pdparams`的后缀，如[1.3 模型恢复训练](#1.3)。
|
||||
**注意：** 在加载待评估模型时，需要指定模型文件的路径，但无需包含文件后缀名，PaddleClas会自动补齐`.pdparams`的后缀，如[1.3 模型恢复训练](#1.3)。
|
||||
|
||||
<a name="2"></a>
|
||||
## 2. 基于Linux+GPU的模型训练与评估
|
||||
|
|
@ -138,24 +137,12 @@ python tools/eval.py \
|
|||
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
|
||||
python -m paddle.distributed.launch \
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml
|
||||
```
|
||||
|
||||
其中，`-c`用于指定配置文件的路径，可通过配置文件修改相关训练配置信息，也可以通过添加`-o`参数来更新配置：
|
||||
|
||||
```bash
|
||||
python -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml \
|
||||
-o pretrained_model="" \
|
||||
-o use_gpu=True
|
||||
```
|
||||
`-o`用于指定需要修改或者添加的参数，其中`-o pretrained_model=""`表示不使用预训练模型，`-o use_gpu=True`表示使用GPU进行训练。
|
||||
|
||||
输出日志信息的格式同上，详见[1.1 模型训练](#1.1)。
|
||||
|
||||
### 2.2 模型微调
|
||||
|
|
@ -165,14 +152,14 @@ python -m paddle.distributed.launch \
|
|||
```
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
|
||||
python -m paddle.distributed.launch \
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml \
|
||||
-o pretrained_model="./pretrained/MobileNetV3_large_x1_0_pretrained"
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Arch.pretrained=True
|
||||
```
|
||||
|
||||
其中`pretrained_model`用于设置加载预训练权重文件的路径，使用时需要换成自己的预训练模型权重文件路径，也可以直接在配置文件中修改该路径。
|
||||
其中`Arch.pretrained`为`True`或`False`，当然也可以设置加载预训练权重文件的路径，使用时需要换成自己的预训练模型权重文件路径，也可以直接在配置文件中修改该路径。
|
||||
|
||||
30分钟玩转PaddleClas[尝鲜版](./quick_start_new_user.md)与[进阶版](./quick_start_professional.md)中包含大量模型微调的示例，可以参考该章节在特定的数据集上进行模型微调。
|
||||
|
||||
|
|
@ -185,16 +172,16 @@ python -m paddle.distributed.launch \
|
|||
```
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
|
||||
python -m paddle.distributed.launch \
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml \
|
||||
-o checkpoints="./output/MobileNetV3_large_x1_0/5/ppcls" \
|
||||
-o last_epoch=5 \
|
||||
-o use_gpu=True
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Global.checkpoints="./output/MobileNetV3_large_x1_0/epoch_5" \
|
||||
-o Optimizer.lr.last_epoch=5 \
|
||||
-o Global.device=gpu
|
||||
```
|
||||
|
||||
其中配置文件不需要做任何修改，只需要在训练时设置`checkpoints`参数与`last_epoch`参数即可，该参数表示加载的断点权重文件路径，使用该参数会同时加载保存的模型参数权重和学习率、优化器等信息，详见[1.3 模型恢复训练](#1.3)。
|
||||
其中配置文件不需要做任何修改，只需要在训练时设置`Global.checkpoints`参数与`Optimizer.lr.last_epoch`参数即可，该参数表示加载的断点权重文件路径，使用该参数会同时加载保存的模型参数权重和学习率、优化器等信息，详见[1.3 模型恢复训练](#1.3)。
|
||||
|
||||
|
||||
### 2.4 模型评估
|
||||
|
|
@ -202,10 +189,11 @@ python -m paddle.distributed.launch \
|
|||
可以通过以下命令进行模型评估。
|
||||
|
||||
```bash
|
||||
python tools/eval.py \
|
||||
-c ./configs/quick_start/MobileNetV3_large_x1_0_finetune.yaml \
|
||||
-o pretrained_model="./output/MobileNetV3_large_x1_0/best_model/ppcls"\
|
||||
-o load_static_weights=False
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python3 -m paddle.distributed.launch \
|
||||
tools/eval.py \
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Global.pretrained_model=./output/MobileNetV3_large_x1_0/best_model
|
||||
```
|
||||
|
||||
参数说明详见[1.4 模型评估](#1.4)。
|
||||
|
|
@ -217,26 +205,16 @@ python tools/eval.py \
|
|||
模型训练完成之后，可以加载训练得到的预训练模型，进行模型预测。在模型库的 `tools/infer/infer.py` 中提供了完整的示例，只需执行下述命令即可完成模型预测：
|
||||
|
||||
```python
|
||||
python tools/infer/infer.py \
|
||||
-i 待预测的图片文件路径 \
|
||||
--model MobileNetV3_large_x1_0 \
|
||||
--pretrained_model "./output/MobileNetV3_large_x1_0/best_model/ppcls" \
|
||||
--use_gpu True \
|
||||
--class_num 1000
|
||||
|
||||
python3 tools/infer.py \
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Infer.infer_imgs=dataset/flowers102/jpg/image_00001.jpg \
|
||||
-o Global.pretrained_model=./output/MobileNetV3_large_x1_0/best_model
|
||||
```
|
||||
|
||||
参数说明：
|
||||
+ `image_file`(简写 i)：待预测的图片文件路径或者批量预测时的图片文件夹，如 `./test.jpeg`
|
||||
+ `model`：模型名称，如 `MobileNetV3_large_x1_0`
|
||||
+ `pretrained_model`：模型权重文件路径，如 `./output/MobileNetV3_large_x1_0/best_model/ppcls`
|
||||
+ `use_gpu` : 是否开启GPU训练，默认值：`True`
|
||||
+ `class_num` : 类别数，默认为1000，需要根据自己的数据进行修改。
|
||||
+ `resize_short`: 对输入图像进行等比例缩放，表示最短边的尺寸，默认值：`256`
|
||||
+ `resize`: 对`resize_short`操作后的进行居中裁剪，表示裁剪的尺寸，默认值：`224`
|
||||
+ `pre_label_image` : 是否对图像数据进行预标注，默认值：`False`
|
||||
+ `pre_label_out_idr` : 预标注图像数据的输出文件夹，当`pre_label_image=True`时，会在该文件夹下面生成很多个子文件夹，每个文件夹名称为类别id，其中存储模型预测属于该类别的所有图像。
|
||||
|
||||
**注意**: 如果使用`Transformer`系列模型，如`DeiT_***_384`, `ViT_***_384`等，请注意模型的输入数据尺寸，需要设置参数`resize_short=384`, `resize=384`。
|
||||
+ `Infer.infer_imgs`：待预测的图片文件路径或者批量预测时的图片文件夹。
|
||||
+ `Global.pretrained_model`：模型权重文件路径，如 `./output/MobileNetV3_large_x1_0/best_model`
|
||||
|
||||
|
||||
<a name="model_inference"></a>
|
||||
|
|
@ -246,42 +224,39 @@ python tools/infer/infer.py \
|
|||
首先，对训练好的模型进行转换：
|
||||
|
||||
```bash
|
||||
python tools/export_model.py \
|
||||
--model MobileNetV3_large_x1_0 \
|
||||
--pretrained_model ./output/MobileNetV3_large_x1_0/best_model/ppcls \
|
||||
--output_path ./inference \
|
||||
--class_dim 1000
|
||||
python3 tools/export_model.py \
|
||||
-c ./ppcls/configs/quick_start/MobileNetV3_large_x1_0.yaml \
|
||||
-o Global.pretrained_model=output/MobileNetV3_large_x1_0/best_model
|
||||
```
|
||||
|
||||
其中，参数`--model`用于指定模型名称，`--pretrained_model`用于指定模型文件路径，该路径仍无需包含模型文件后缀名（如[1.3 模型恢复训练](#1.3)），`--output_path`用于指定转换后模型的存储路径，`class_dim`表示模型所包含的类别数，默认为1000。
|
||||
|
||||
**注意**：
|
||||
1. `--output_path`表示输出的inference模型文件夹路径，若`--output_path=./inference`，则会在`inference`文件夹下生成`inference.pdiparams`、`inference.pdmodel`和`inference.pdiparams.info`文件。
|
||||
2. 可以通过设置参数`--img_size`指定模型输入图像的`shape`，默认为`224`，表示图像尺寸为`224*224`，请根据实际情况修改。
|
||||
其中，`Global.pretrained_model`用于指定模型文件路径，该路径仍无需包含模型文件后缀名（如[1.3 模型恢复训练](#1.3)）。
|
||||
|
||||
|
||||
上述命令将生成模型结构文件（`inference.pdmodel`）和模型权重文件（`inference.pdiparams`），然后可以使用预测引擎进行推理：
|
||||
|
||||
进入deploy目录下：
|
||||
|
||||
```bash
|
||||
python tools/infer/predict.py \
|
||||
--image_file 图片路径 \
|
||||
--model_file "./inference/inference.pdmodel" \
|
||||
--params_file "./inference/inference.pdiparams" \
|
||||
--use_gpu=True \
|
||||
--use_tensorrt=False
|
||||
cd deploy
|
||||
```
|
||||
|
||||
执行命令进行预测，由于默认class_id_map_file是ImageNet数据集的映射文件，所以此处需要置None。
|
||||
|
||||
```bash
|
||||
python3 python/predict_cls.py \
|
||||
-c configs/inference_cls.yaml \
|
||||
-o Global.infer_imgs=../dataset/flowers102/jpg/image_00001.jpg \
|
||||
-o Global.inference_model_dir=../inference/ \
|
||||
-o PostProcess.class_id_map_file=None
|
||||
|
||||
|
||||
其中：
|
||||
+ `image_file`：待预测的图片文件路径，如 `./test.jpeg`
|
||||
+ `model_file`：模型结构文件路径，如 `./inference/inference.pdmodel`
|
||||
+ `params_file`：模型权重文件路径，如 `./inference/inference.pdiparams`
|
||||
+ `use_tensorrt`：是否使用 TesorRT 预测引擎，默认值：`True`
|
||||
+ `use_gpu`：是否使用 GPU 预测，默认值：`True`
|
||||
+ `enable_mkldnn`：是否启用`MKL-DNN`加速，默认为`False`。注意`enable_mkldnn`与`use_gpu`同时为`True`时，将忽略`enable_mkldnn`，而使用GPU运行。
|
||||
+ `resize_short`: 对输入图像进行等比例缩放，表示最短边的尺寸，默认值：`256`
|
||||
+ `resize`: 对`resize_short`操作后的进行居中裁剪，表示裁剪的尺寸，默认值：`224`
|
||||
+ `enable_calc_topk`: 是否计算预测结果的Topk精度指标，默认为`False`，
|
||||
+ `gt_label_path`: 图像文件名以及真值标签文件，当`enable_calc_topk`为True时生效，用于读取待预测的图像列表及其标签。
|
||||
+ `Global.infer_imgs`：待预测的图片文件路径。
|
||||
+ `Global.inference_model_dir`：inference模型结构文件路径，如 `../inference/inference.pdmodel`
|
||||
+ `Global.use_tensorrt`：是否使用 TesorRT 预测引擎，默认值：`False`
|
||||
+ `Global.use_gpu`：是否使用 GPU 预测，默认值：`True`
|
||||
+ `Global.enable_mkldnn`：是否启用`MKL-DNN`加速，默认为`False`。注意`enable_mkldnn`与`use_gpu`同时为`True`时，将忽略`enable_mkldnn`，而使用GPU运行。
|
||||
+ `Global.use_fp16`：是否启用`FP16`，默认为`False`。
|
||||
|
||||
|
||||
**注意**: 如果使用`Transformer`系列模型，如`DeiT_***_384`, `ViT_***_384`等，请注意模型的输入数据尺寸，需要设置参数`resize_short=384`, `resize=384`。
|
||||
|
||||
* 如果你希望评测模型速度，建议使用该脚本(`tools/infer/predict.py`)，同时开启TensorRT加速预测。
|
||||
* 如果你希望提升评测模型速度，使用gpu评测时，建议开启TensorRT加速预测，使用cpu评测时，建议开启MKL-DNN加速预测。
|
||||
|
|
|
|||
|
|
@ -51,34 +51,30 @@ train/n01440764/n01440764_10027.JPEG 0
|
|||
对于读入的数据，需要通过数据转换，将原始的图像数据进行转换。训练时，标准的数据预处理包含：`DecodeImage`, `RandCropImage`, `RandFlipImage`, `NormalizeImage`, `ToCHWImage`。在配置文件中体现如下，数据预处理主要包含在`transforms`字段中，以列表形式呈现，会按照顺序对数据依次做这些转换。
|
||||
|
||||
```yaml
|
||||
TRAIN:
|
||||
batch_size: 256 # 所有训练设备上的总batch size
|
||||
num_workers: 4 # 训练时每块设备上的进程数
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt" # 训练标签文件
|
||||
data_dir: "./dataset/ILSVRC2012/" # 训练图片文件夹
|
||||
shuffle_seed: 0 # 随机打散的种子数
|
||||
transforms:
|
||||
- DecodeImage: # 对图像文件进行解码，转成numpy矩阵
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/ILSVRC2012/
|
||||
cls_label_path: ./dataset/ILSVRC2012/train_list.txt
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage: # 对图像做随机裁剪
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage: # 对图像做随机翻转
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage: # 对图像做归一化
|
||||
scale: 1./255.
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage: # 将图像从HWC格式转成CHW格式
|
||||
mix:
|
||||
- MixupOperator: # mixup数据增广，在全局配置use_mix=True时生效
|
||||
alpha: 0.2
|
||||
```
|
||||
|
||||
PaddleClas中也包含了`AutoAugment`, `RandAugment`等数据增广方法，也可以通过在配置文件中配置，从而添加到训练过程的数据预处理中。每个数据转换的方法均以类实现，方便迁移和复用，更多的数据处理具体实现过程可以参考：`ppcls/data/imaug/operators.py`。
|
||||
PaddleClas中也包含了`AutoAugment`, `RandAugment`等数据增广方法，也可以通过在配置文件中配置，从而添加到训练过程的数据预处理中。每个数据转换的方法均以类实现，方便迁移和复用，更多的数据处理具体实现过程可以参考`ppcls/data/preprocess/ops/`下的代码。
|
||||
|
||||
对于组成一个batch的数据，也可以使用mixup或者cutmix等方法进行数据增广。PaddleClas中集成了`MixupOperator`, `CutmixOperator`, `FmixOperator`等基于batch的数据增广方法，可以在配置文件中配置mix参数进行配置，更加具体的实现可以参考`ppcls/data/imaug/batch_operators.py`。
|
||||
对于组成一个batch的数据，也可以使用mixup或者cutmix等方法进行数据增广。PaddleClas中集成了`MixupOperator`, `CutmixOperator`, `FmixOperator`等基于batch的数据增广方法，可以在配置文件中配置mix参数进行配置，更加具体的实现可以参考`ppcls/data/preprocess/batch_ops/batch_operators.py`。
|
||||
|
||||
图像分类中，数据后处理主要为`argmax`操作，在此不再赘述。
|
||||
|
||||
|
|
@ -87,69 +83,47 @@ PaddleClas中也包含了`AutoAugment`, `RandAugment`等数据增广方法，也
|
|||
在配置文件中，模型结构定义如下
|
||||
|
||||
```yaml
|
||||
ARCHITECTURE:
|
||||
name: "EfficientNetB0"
|
||||
params: # 模型需要传入的额外参数，如果没有可不填
|
||||
padding_type : "SAME"
|
||||
override_params:
|
||||
drop_connect_rate: 0.1
|
||||
Arch:
|
||||
name: ResNet50
|
||||
pretrained: False
|
||||
use_ssld: False
|
||||
```
|
||||
|
||||
`Arch.name`表示模型名称，`Arch.pretrained`表示是否添加预训练模型。所有的模型名称均在`ppcls/arch/backbone/__init__.py`中定义。
|
||||
|
||||
`ARCHITECTURE.name`表示模型名称，`ARCHITECTURE.params`表示需要额外传入的参数，默认为空。所有的模型名称均在`/ppcls/modeling/architectures/__init__.py`中定义。
|
||||
|
||||
对应的，在`tools/program.py`中，通过`create_model`方法创建模型对象。
|
||||
对应的，在`ppcls/arch/__init__.py`中，通过`build_model`方法创建模型对象。
|
||||
|
||||
```python
|
||||
def create_model(architecture, classes_num):
|
||||
name = architecture["name"]
|
||||
params = architecture.get("params", {})
|
||||
return architectures.__dict__[name](class_dim=classes_num, **params)
|
||||
def build_model(config):
|
||||
config = copy.deepcopy(config)
|
||||
model_type = config.pop("name")
|
||||
mod = importlib.import_module(__name__)
|
||||
arch = getattr(mod, model_type)(**config)
|
||||
return arch
|
||||
```
|
||||
|
||||
* 损失函数
|
||||
|
||||
PaddleClas中，包含了`CELoss`, `MixCELoss`, `GoogLeNetLoss`, `JSDivLoss`, `MultiLabelLoss`等损失函数，均定义在`ppcls/modeling/loss.py`中。
|
||||
PaddleClas中，包含了`CELoss`, `JSDivLoss`, `TripletLoss`, `CenterLoss`等损失函数，均定义在`ppcls/loss`中。
|
||||
|
||||
在`tools/program.py`文件中，使用`create_loss`构建模型的损失函数，不同训练策略中所需要的损失函数与计算方法不同，PaddleClas在构建损失函数过程中，主要考虑了以下几个因素。
|
||||
在`ppcls/loss/__init__.py`文件中，使用`CombinedLoss`来构建及合并损失函数，不同训练策略中所需要的损失函数与计算方法不同，PaddleClas在构建损失函数过程中，主要考虑了以下几个因素。
|
||||
|
||||
1. 是否使用label smooth
|
||||
2. 是否使用mixup或者cutmix
|
||||
3. 是否使用蒸馏方法进行训练
|
||||
4. 是否进行多标签训练
|
||||
4. 是否是训练metric learning
|
||||
|
||||
```python
|
||||
def create_loss(feeds,
|
||||
out,
|
||||
architecture,
|
||||
classes_num=1000,
|
||||
epsilon=None,
|
||||
use_mix=False,
|
||||
use_distillation=False,
|
||||
multilabel=False):
|
||||
if architecture["name"] == "GoogLeNet":
|
||||
assert len(out) == 3, "GoogLeNet should have 3 outputs"
|
||||
loss = GoogLeNetLoss(class_dim=classes_num, epsilon=epsilon)
|
||||
return loss(out[0], out[1], out[2], feeds["label"])
|
||||
|
||||
if use_distillation:
|
||||
assert len(out) == 2, ("distillation output length must be 2, "
|
||||
"but got {}".format(len(out)))
|
||||
loss = JSDivLoss(class_dim=classes_num, epsilon=epsilon)
|
||||
return loss(out[1], out[0])
|
||||
用户可以在配置文件中指定损失函数的类型及权重，如在训练中添加TripletLossV2，配置文件如下：
|
||||
|
||||
if use_mix:
|
||||
loss = MixCELoss(class_dim=classes_num, epsilon=epsilon)
|
||||
feed_y_a = feeds['y_a']
|
||||
feed_y_b = feeds['y_b']
|
||||
feed_lam = feeds['lam']
|
||||
return loss(out, feed_y_a, feed_y_b, feed_lam)
|
||||
else:
|
||||
if not multilabel:
|
||||
loss = CELoss(class_dim=classes_num, epsilon=epsilon)
|
||||
else:
|
||||
loss = MultiLabelLoss(class_dim=classes_num, epsilon=epsilon)
|
||||
return loss(out, feeds["label"])
|
||||
```yaml
|
||||
Loss:
|
||||
Train:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
- TripletLossV2:
|
||||
weight: 1.0
|
||||
margin: 0.5
|
||||
```
|
||||
|
||||
* 优化器和学习率衰减、权重衰减策略
|
||||
|
|
@ -158,48 +132,53 @@ def create_loss(feeds,
|
|||
|
||||
权重衰减策略是一种比较常用的正则化方法，主要用于防止模型过拟合。PaddleClas中提供了`L1Decay`和`L2Decay`两种权重衰减策略。
|
||||
|
||||
学习率衰减是图像分类任务中必不可少的精度提升训练方法，PaddleClas目前支持`Cosine`, `Piecewise`, `CosineWarmup`, `ExponentialWarmup`等学习率衰减策略。
|
||||
学习率衰减是图像分类任务中必不可少的精度提升训练方法，PaddleClas目前支持`Cosine`, `Piecewise`, `Linear`等学习率衰减策略。
|
||||
|
||||
在配置文件中，优化器和权重衰减策略可以通过以下的字段进行配置。
|
||||
在配置文件中，优化器、权重衰减策略、学习率衰减策略可以通过以下的字段进行配置。
|
||||
|
||||
```yaml
|
||||
OPTIMIZER:
|
||||
function: 'Momentum' # Momentum优化器
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2' # L1 means L1Decay, L2 means L2Decay
|
||||
factor: 0.00010
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Piecewise
|
||||
learning_rate: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
values: [0.1, 0.01, 0.001, 0.0001]
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.0001
|
||||
```
|
||||
|
||||
学习率衰减策略可以通过以下的字段进行配置。
|
||||
|
||||
```yaml
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise' # Piecewise学习率衰减策略
|
||||
params:
|
||||
lr: 0.1 # 初始学习率
|
||||
decay_epochs: [30, 60, 90] # 学习率下降时对应的epoch数量
|
||||
gamma: 0.1 # 学习率衰减倍数
|
||||
```
|
||||
|
||||
在`tools/program.py`中使用`create_optimizer`创建优化器和学习率对象。
|
||||
在`ppcls/optimizer/__init__.py`中使用`build_optimizer`创建优化器和学习率对象。
|
||||
|
||||
```python
|
||||
def create_optimizer(config, parameter_list=None):
|
||||
# create learning_rate instance
|
||||
lr_config = config['LEARNING_RATE']
|
||||
lr_config['params'].update({
|
||||
'epochs': config['epochs'],
|
||||
'step_each_epoch':
|
||||
config['total_images'] // config['TRAIN']['batch_size'],
|
||||
})
|
||||
lr = LearningRateBuilder(**lr_config)()
|
||||
|
||||
# create optimizer instance
|
||||
opt_config = config['OPTIMIZER']
|
||||
opt = OptimizerBuilder(**opt_config)
|
||||
return opt(lr, parameter_list), lr
|
||||
def build_optimizer(config, epochs, step_each_epoch, parameters):
|
||||
config = copy.deepcopy(config)
|
||||
# step1 build lr
|
||||
lr = build_lr_scheduler(config.pop('lr'), epochs, step_each_epoch)
|
||||
logger.debug("build lr ({}) success..".format(lr))
|
||||
# step2 build regularization
|
||||
if 'regularizer' in config and config['regularizer'] is not None:
|
||||
reg_config = config.pop('regularizer')
|
||||
reg_name = reg_config.pop('name') + 'Decay'
|
||||
reg = getattr(paddle.regularizer, reg_name)(**reg_config)
|
||||
else:
|
||||
reg = None
|
||||
logger.debug("build regularizer ({}) success..".format(reg))
|
||||
# step3 build optimizer
|
||||
optim_name = config.pop('name')
|
||||
if 'clip_norm' in config:
|
||||
clip_norm = config.pop('clip_norm')
|
||||
grad_clip = paddle.nn.ClipGradByNorm(clip_norm=clip_norm)
|
||||
else:
|
||||
grad_clip = None
|
||||
optim = getattr(optimizer, optim_name)(learning_rate=lr,
|
||||
weight_decay=reg,
|
||||
grad_clip=grad_clip,
|
||||
**config)(parameters=parameters)
|
||||
logger.debug("build optimizer ({}) success..".format(optim))
|
||||
return optim, lr
|
||||
```
|
||||
|
||||
不同优化器和权重衰减策略均以类的形式实现，具体实现可以参考文件`ppcls/optimizer/optimizer.py`；不同的学习率衰减策略可以参考文件`ppcls/optimizer/learning_rate.py`。
|
||||
|
|
@ -210,27 +189,22 @@ def create_optimizer(config, parameter_list=None):
|
|||
模型在训练的时候，可以设置模型保存的间隔，也可以选择每隔若干个epoch对验证集进行评估，从而可以保存在验证集上精度最佳的模型。配置文件中，可以通过下面的字段进行配置。
|
||||
|
||||
```yaml
|
||||
save_interval: 1 # 模型保存的epoch间隔
|
||||
validate: True # 是否进行训练时评估
|
||||
valid_interval: 1 # 评估的epoch间隔
|
||||
Global:
|
||||
save_interval: 1 # 模型保存的epoch间隔
|
||||
eval_during_train: True # 是否进行训练时评估
|
||||
eval_interval: 1 # 评估的epoch间隔
|
||||
```
|
||||
|
||||
模型存储是通过 Paddle 框架的 `paddle.save()` 函数实现的，存储的是模型的 persistable 版本，便于继续训练。具体实现如下
|
||||
|
||||
```python
|
||||
def save_model(net, optimizer, model_path, epoch_id, prefix='ppcls'):
|
||||
# just save model in trainer_id=0
|
||||
if paddle.distributed.get_rank() != 0:
|
||||
return
|
||||
ef save_model(program, model_path, epoch_id, prefix='ppcls'):
|
||||
model_path = os.path.join(model_path, str(epoch_id))
|
||||
_mkdir_if_not_exist(model_path)
|
||||
model_prefix = os.path.join(model_path, prefix)
|
||||
# save student model during distillation
|
||||
_save_student_model(net, model_prefix)
|
||||
|
||||
paddle.save(net.state_dict(), model_prefix + ".pdparams")
|
||||
paddle.save(optimizer.state_dict(), model_prefix + ".pdopt")
|
||||
logger.info("Already save model in {}".format(model_path))
|
||||
paddle.static.save(program, model_prefix)
|
||||
logger.info(
|
||||
logger.coloring("Already save model in {}".format(model_path), "HEADER"))
|
||||
```
|
||||
|
||||
在保存的时候有两点需要注意：
|
||||
|
|
|
|||
|
|
@ -75,20 +75,6 @@ cd ../../
|
|||
|
||||
### 预训练模型下载
|
||||
|
||||
```shell
|
||||
# 创建文件夹pretrained文件夹并进入
|
||||
mkdir pretrained && cd pretrained
|
||||
# 下载预训练模型
|
||||
# 下载ResNet50_vd模型
|
||||
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
|
||||
# 下载ShuffleNetV2_x0_25模型
|
||||
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ShuffleNetV2_x0_25_pretrained.pdparams
|
||||
# 回到PaddleClas主目录
|
||||
cd ..
|
||||
```
|
||||
|
||||
Windows操作如上提示，在PaddleClas根目录下创建相应文件夹，并下载好预训练模型后，放到此文件夹中。
|
||||
|
||||
### 训练模型
|
||||
|
||||
#### 使用CPU进行模型训练
|
||||
|
|
@ -99,20 +85,19 @@ Windows操作如上提示，在PaddleClas根目录下创建相应文件夹，并
|
|||
|
||||
```shell
|
||||
#windows在cmd中进入PaddleClas根目录，执行此命令
|
||||
python tools/train.py -c ./configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml
|
||||
python tools/train.py -c ./ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml
|
||||
```
|
||||
|
||||
- `-c` 参数是指定训练的配置文件路径，训练的具体超参数可查看`yaml`文件
|
||||
- `yaml`文`use_gpu` 参数设置为`False`，即使用CPU进行训练（若不设置，此参数默认为`True`）
|
||||
- `yaml`文`Global.device` 参数设置为`cpu`，即使用CPU进行训练（若不设置，此参数默认为`True`）
|
||||
- `yaml`文件中`epochs`参数设置为20，说明对整个数据集进行20个epoch迭代，预计训练20分钟左右（不同CPU，训练时间略有不同），此时训练模型不充分。若提高训练模型精度，请将此参数设大，如**40**，训练时间也会相应延长
|
||||
|
||||
##### 使用预训练模型
|
||||
|
||||
```shell
|
||||
python tools/train.py -c ./configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml -o pretrained_model="pretrained/ShuffleNetV2_x0_25_pretrained"
|
||||
```
|
||||
python tools/train.py -c ./ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml -o Arch.pretrained=True
|
||||
|
||||
- `-o` 参数加入预训练模型地址，注意：预训练模型路径不要加上：`.pdparams`
|
||||
- `-o` 参数可以选择为True或False，也可以是预训练模型存放路径，当选择为True时，预训练权重会自动下载到本地。注意：若为预训练模型路径，则不要加上：`.pdparams`
|
||||
|
||||
可以使用将使用与不使用预训练模型训练进行对比，观察loss的下降情况。
|
||||
|
||||
|
|
@ -137,7 +122,7 @@ python tools/train.py -c ./configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml
|
|||
##### 不使用预训练模型
|
||||
|
||||
```shell
|
||||
python tools/train.py -c ./configs/quick_start/ResNet50_vd.yaml
|
||||
python3 tools/train.py -c ./ppcls/configs/quick_start/ResNet50_vd.yaml
|
||||
```
|
||||
|
||||
训练完成后，验证集的`Top1 Acc`曲线如下所示，最高准确率为0.2735。训练精度曲线下图所示
|
||||
|
|
@ -149,13 +134,12 @@ python tools/train.py -c ./configs/quick_start/ResNet50_vd.yaml
|
|||
基于ImageNet1k分类预训练模型进行微调，训练脚本如下所示
|
||||
|
||||
```shell
|
||||
python tools/train.py -c ./configs/quick_start/ResNet50_vd_finetune.yaml
|
||||
python3 tools/train.py -c ./ppcls/configs/quick_start/ResNet50_vd.yaml -o Arch.pretrained=True
|
||||
```
|
||||
|
||||
**注**：
|
||||
|
||||
- 此训练脚本使用GPU，如使用CPU可按照上文中[使用CPU进行模型训练](#使用CPU进行模型训练)所示，进行修改
|
||||
- 与[不使用预训练模型](#不使用预训练模型)的`yaml`文件的主要不同，此`ymal`文件中加入 `pretrained_model` 参数，此参数指明预训练模型的位置
|
||||
|
||||
验证集的`Top1 Acc`曲线如下所示，最高准确率为0.9402，加载预训练模型之后，flowers102数据集精度大幅提升，绝对精度涨幅超过65%。
|
||||
|
||||
|
|
@ -167,35 +151,16 @@ python tools/train.py -c ./configs/quick_start/ResNet50_vd_finetune.yaml
|
|||
|
||||
```shell
|
||||
cd $path_to_PaddleClas
|
||||
python tools/infer/infer.py --model ShuffleNetV2_x0_25 -i dataset/flowers102/jpg/image_00001.jpg --pretrained_model output/ShuffleNetV2_x0_25/best_model/ppcls --class_num 102 --use_gpu False
|
||||
python3 tools/infer.py -c ./ppcls/configs/quick_start/new_user/ShuffleNetV2_x0_25.yaml -o Infer.infer_imgs=dataset/flowers102/jpg/image_00001.jpg -o Global.pretrained_model=output/ShuffleNetV2_x0_25/best_model
|
||||
```
|
||||
|
||||
其中主要参数如下：
|
||||
|
||||
- `--model`：训练时使用擦网络模型，如 ShuffleNetV2_x0_25、ResNet50_vd，具体可查看训练时`yaml`文件中**ARCHITECTURE**下 **name**参数的值
|
||||
- `-i`：图像文件路径或者图像所在目录
|
||||
- `--pretrained_model`： 存放的模型权重位置。上述CPU训练过程中，最优模型存放位置如下：`output/ShuffleNetV2_x0_25/best_model/ppcls.pdparams`，此时此参数应如下填写：`output/ShuffleNetV2_x0_25/best_model/ppcls`，去掉`.pdparams`
|
||||
- `--class_num`：为图像类别数，`flowers102`数据集为102类。若用其他数据集，改成相应类别数即可
|
||||
- `--use_gpu`：是否使用GPU
|
||||
|
||||
`-i`输入为单张图像路径，运行成功后，示例结果如下：
|
||||
|
||||
`File:image_00001.jpg, Top-1 result: class id(s): [72], score(s): [0.03]`
|
||||
`[{'class_ids': [76, 65, 34, 9, 69], 'scores': [0.91762, 0.01801, 0.00833, 0.0071, 0.00669], 'file_name': 'dataset/flowers102/jpg/image_00001.jpg', 'label_names': []}]`
|
||||
|
||||
`-i`输入为图像集所在目录，运行成功后，示例结果如下：
|
||||
|
||||
```txt
|
||||
File:image_02993.jpg, Top-1 result: class id(s): [77], score(s): [0.02]
|
||||
File:image_00448.jpg, Top-1 result: class id(s): [77], score(s): [0.02]
|
||||
File:image_08001.jpg, Top-1 result: class id(s): [77], score(s): [0.01]
|
||||
File:image_00804.jpg, Top-1 result: class id(s): [100], score(s): [0.02]
|
||||
File:image_01842.jpg, Top-1 result: class id(s): [100], score(s): [0.02]
|
||||
File:image_02790.jpg, Top-1 result: class id(s): [70], score(s): [0.05]
|
||||
File:image_03412.jpg, Top-1 result: class id(s): [100], score(s): [0.02]
|
||||
File:image_05196.jpg, Top-1 result: class id(s): [77], score(s): [0.02]
|
||||
File:image_06860.jpg, Top-1 result: class id(s): [70], score(s): [0.03]
|
||||
File:image_05312.jpg, Top-1 result: class id(s): [77], score(s): [0.02]
|
||||
File:image_05930.jpg, Top-1 result: class id(s): [100], score(s): [0.02]
|
||||
File:image_05711.jpg, Top-1 result: class id(s): [77], score(s): [0.01]
|
||||
File:image_01180.jpg, Top-1 result: class id(s): [70], score(s): [0.03]
|
||||
[{'class_ids': [76, 65, 34, 9, 69], 'scores': [0.91762, 0.01801, 0.00833, 0.0071, 0.00669], 'file_name': 'dataset/flowers102/jpg/image_00001.jpg', 'label_names': []}, {'class_ids': [76, 69, 34, 28, 9], 'scores': [0.77122, 0.06295, 0.02537, 0.02531, 0.0251], 'file_name': 'dataset/flowers102/jpg/image_00002.jpg', 'label_names': []}, {'class_ids': [99, 76, 81, 85, 16], 'scores': [0.26374, 0.20423, 0.07818, 0.06042, 0.05499], 'file_name': 'dataset/flowers102/jpg/image_00003.jpg', 'label_names': []}, {'class_ids': [9, 37, 34, 24, 76], 'scores': [0.17784, 0.16651, 0.14539, 0.12096, 0.04816], 'file_name': 'dataset/flowers102/jpg/image_00004.jpg', 'label_names': []}, {'class_ids': [76, 66, 91, 16, 13], 'scores': [0.95494, 0.00688, 0.00596, 0.00352, 0.00308], 'file_name': 'dataset/flowers102/jpg/image_00005.jpg', 'label_names': []}, {'class_ids': [76, 66, 34, 8, 43], 'scores': [0.44425, 0.07487, 0.05609, 0.05609, 0.03667], 'file_name': 'dataset/flowers102/jpg/image_00006.jpg', 'label_names': []}, {'class_ids': [86, 93, 81, 22, 21], 'scores': [0.44714, 0.13582, 0.07997, 0.0514, 0.03497], 'file_name': 'dataset/flowers102/jpg/image_00007.jpg', 'label_names': []}, {'class_ids': [13, 76, 81, 18, 97], 'scores': [0.26771, 0.1734, 0.06576, 0.0451, 0.03986], 'file_name': 'dataset/flowers102/jpg/image_00008.jpg', 'label_names': []}, {'class_ids': [34, 76, 8, 5, 9], 'scores': [0.67224, 0.31896, 0.00241, 0.00227, 0.00102], 'file_name': 'dataset/flowers102/jpg/image_00009.jpg', 'label_names': []}, {'class_ids': [76, 34, 69, 65, 66], 'scores': [0.95185, 0.01101, 0.00875, 0.00452, 0.00406], 'file_name': 'dataset/flowers102/jpg/image_00010.jpg', 'label_names': []}]
|
||||
```
|
||||
其中，列表的长度为batch_size的大小。
|
||||
|
|
|
|||
|
|
@ -25,36 +25,6 @@ tar -xf CIFAR100.tar
|
|||
cd ../
|
||||
```
|
||||
|
||||
#### 1.1.2 准备NUS-WIDE-SCENE
|
||||
|
||||
* 创建并进入`dataset/NUS-WIDE-SCENE`目录，下载并解压NUS-WIDE-SCENE数据集。
|
||||
|
||||
```shell
|
||||
mkdir dataset/NUS-WIDE-SCENE
|
||||
cd dataset/NUS-WIDE-SCENE
|
||||
wget https://paddle-imagenet-models-name.bj.bcebos.com/data/NUS-SCENE-dataset.tar
|
||||
tar -xf NUS-SCENE-dataset.tar
|
||||
```
|
||||
|
||||
* 返回`PaddleClas`根目录
|
||||
|
||||
```
|
||||
cd ../../
|
||||
```
|
||||
|
||||
### 1.2 模型准备
|
||||
|
||||
通过下面的命令下载所需要的预训练模型。
|
||||
|
||||
```bash
|
||||
mkdir pretrained
|
||||
cd pretrained
|
||||
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
|
||||
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_ssld_pretrained.pdparams
|
||||
wget https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV3_large_x1_0_pretrained.pdparams
|
||||
cd ../
|
||||
```
|
||||
|
||||
|
||||
## 二、模型训练
|
||||
|
||||
|
|
@ -69,8 +39,8 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
|
|||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \
|
||||
-o model_save_dir="output_CIFAR"
|
||||
-c ./ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \
|
||||
-o Global.output_dir="output_CIFAR"
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -86,8 +56,9 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
|
|||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/professional/ResNet50_vd_CIFAR100_finetune.yaml \
|
||||
-o model_save_dir="output_CIFAR"
|
||||
-c ./ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \
|
||||
-o Global.output_dir="output_CIFAR" \
|
||||
-o Arch.pretrained=True
|
||||
```
|
||||
|
||||
验证集最高准确率为0.718左右，加载预训练模型之后，CIFAR100数据集精度大幅提升，绝对精度涨幅30\%。
|
||||
|
|
@ -99,8 +70,10 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
|
|||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/professional/ResNet50_vd_ssld_CIFAR100_finetune.yaml \
|
||||
-o model_save_dir="output_CIFAR"
|
||||
-c ./ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \
|
||||
-o Global.output_dir="output_CIFAR" \
|
||||
-o Arch.pretrained=True \
|
||||
-o Arch.use_ssld=True
|
||||
```
|
||||
|
||||
最终CIFAR100验证集上精度指标为0.73，相对于79.12\%预训练模型的微调结构，新数据集指标可以再次提升1.2\%。
|
||||
|
|
@ -112,31 +85,14 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
|
|||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml \
|
||||
-o model_save_dir="output_CIFAR"
|
||||
-c ./ppcls/configs/quick_start/professional/MobileNetV3_large_x1_0_CIFAR100_finetune.yaml \
|
||||
-o Global.output_dir="output_CIFAR" \
|
||||
-o Arch.pretrained=True
|
||||
```
|
||||
|
||||
验证集最高准确率为0.601左右, 较ResNet50_vd低近12%。
|
||||
|
||||
|
||||
### 2.2 多标签训练
|
||||
|
||||
* 基于ImageNet1k分类预训练模型进行微调NUS-WIDE-SCENE数据集，该是数据集NUS-WIDE的一个子集，类别数目为33类，图片总数是17463张，训练脚本如下所示。
|
||||
|
||||
```shell
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/ResNet50_vd_multilabel.yaml \
|
||||
-o model_save_dir="output_NUS-WIDE-SCENE"
|
||||
```
|
||||
|
||||
训练10epoch之后，验证集最好的准确率应该在0.95左右。
|
||||
|
||||
* 零基础训练(不加载预训练模型)只需要将配置文件中的`pretrained_model`置为`""`即可。
|
||||
|
||||
|
||||
## 三、数据增广
|
||||
|
||||
PaddleClas包含了很多数据增广的方法，如Mixup、Cutout、RandomErasing等，具体的方法可以参考[数据增广的章节](../advanced_tutorials/image_augmentation/ImageAugment.md)。
|
||||
|
|
@ -150,8 +106,8 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
|
|||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml \
|
||||
-o model_save_dir="output_CIFAR"
|
||||
-c ./ppcls/configs/quick_start/professional/ResNet50_vd_mixup_CIFAR100_finetune.yaml \
|
||||
-o Global.output_dir="output_CIFAR"
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -161,7 +117,7 @@ python3 -m paddle.distributed.launch \
|
|||
|
||||
* **注意**
|
||||
|
||||
* 其他数据增广的配置文件可以参考`configs/DataAugment`中的配置文件。
|
||||
* 其他数据增广的配置文件可以参考`ppcls/configs/DataAugment`中的配置文件。
|
||||
|
||||
* 训练CIFAR100的迭代轮数较少，因此进行训练时，验证集的精度指标可能会有1\%左右的波动。
|
||||
|
||||
|
|
@ -172,18 +128,42 @@ python3 -m paddle.distributed.launch \
|
|||
PaddleClas包含了自研的SSLD知识蒸馏方案，具体的内容可以参考[知识蒸馏章节](../advanced_tutorials/distillation/distillation.md)本小节将尝试使用知识蒸馏技术对MobileNetV3_large_x1_0模型进行训练，使用`2.1.2小节`训练得到的ResNet50_vd模型作为蒸馏所用的教师模型，首先将`2.1.2小节`训练得到的ResNet50_vd模型保存到指定目录，脚本如下。
|
||||
|
||||
```shell
|
||||
cp -r output_CIFAR/ResNet50_vd/best_model/ ./pretrained/CIFAR100_R50_vd_final/
|
||||
mkdir pretrained
|
||||
cp -r output_CIFAR/ResNet50_vd/best_model.pdparams ./pretrained/
|
||||
```
|
||||
|
||||
配置文件中数据数量、模型结构、预训练地址以及训练的数据配置如下：
|
||||
配置文件中模型名字、教师模型哈学生模型的配置、预训练地址配置以及freeze_params配置如下，其中freeze_params_list中的两个值分别代表教师模型和学生模型是否冻结参数训练。
|
||||
|
||||
```yaml
|
||||
total_images: 50000
|
||||
ARCHITECTURE:
|
||||
name: 'ResNet50_vd_distill_MobileNetV3_large_x1_0'
|
||||
pretrained_model:
|
||||
- "./pretrained/CIFAR100_R50_vd_final/ppcls"
|
||||
- "./pretrained/MobileNetV3_large_x1_0_pretrained/”
|
||||
Arch:
|
||||
name: "DistillationModel"
|
||||
# if not null, its lengths should be same as models
|
||||
pretrained_list:
|
||||
# if not null, its lengths should be same as models
|
||||
freeze_params_list:
|
||||
- True
|
||||
- False
|
||||
models:
|
||||
- Teacher:
|
||||
name: ResNet50_vd
|
||||
pretrained: "./pretrained/best_model"
|
||||
- Student:
|
||||
name: MobileNetV3_large_x1_0
|
||||
pretrained: True
|
||||
```
|
||||
|
||||
Loss配置如下，其中训练Loss是学生模型的输出和教师模型的输出的交叉熵、验证Loss是学生模型的输出和真实标签的交叉熵。
|
||||
```yaml
|
||||
Loss:
|
||||
Train:
|
||||
- DistillationCELoss:
|
||||
weight: 1.0
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
Eval:
|
||||
- DistillationGTCELoss:
|
||||
weight: 1.0
|
||||
model_names: ["Student"]
|
||||
```
|
||||
|
||||
最终的训练脚本如下所示。
|
||||
|
|
@ -193,8 +173,8 @@ export CUDA_VISIBLE_DEVICES=0,1,2,3
|
|||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml \
|
||||
-o model_save_dir="output_CIFAR"
|
||||
-c ./ppcls/configs/quick_start/professional/R50_vd_distill_MV3_large_x1_0_CIFAR100.yaml \
|
||||
-o Global.output_dir="output_CIFAR"
|
||||
|
||||
```
|
||||
|
||||
|
|
@ -217,20 +197,19 @@ python3 -m paddle.distributed.launch \
|
|||
|
||||
```bash
|
||||
python3 tools/eval.py \
|
||||
-c ./configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \
|
||||
-o pretrained_model="./output_CIFAR/ResNet50_vd/best_model/ppcls"
|
||||
-c ./ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \
|
||||
-o Global.pretrained_model="output_CIFAR/ResNet50_vd/best_model"
|
||||
```
|
||||
|
||||
#### 5.1.2 单标签分类模型预测
|
||||
|
||||
模型训练完成之后，可以加载训练得到的预训练模型，进行模型预测。在模型库的 `tools/infer/infer.py` 中提供了完整的示例，只需执行下述命令即可完成模型预测：
|
||||
模型训练完成之后，可以加载训练得到的预训练模型，进行模型预测。在模型库的 `tools/infer.py` 中提供了完整的示例，只需执行下述命令即可完成模型预测：
|
||||
|
||||
```python
|
||||
python3 tools/infer/infer.py \
|
||||
-i "./dataset/CIFAR100/test/0/0001.png" \
|
||||
--model ResNet50_vd \
|
||||
--pretrained_model "./output_CIFAR/ResNet50_vd/best_model/ppcls" \
|
||||
--use_gpu True
|
||||
python3 tools/infer.py \
|
||||
-c ./ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \
|
||||
-o Infer.infer_imgs=./dataset/CIFAR100/test/0/0001.png \
|
||||
-o Global.pretrained_model=output_CIFAR/ResNet50_vd/best_model
|
||||
```
|
||||
|
||||
|
||||
|
|
@ -241,53 +220,41 @@ python3 tools/infer/infer.py \
|
|||
|
||||
```bash
|
||||
python3 tools/export_model.py \
|
||||
--model ResNet50_vd \
|
||||
--pretrained_model ./output_CIFAR/ResNet50_vd/best_model/ppcls \
|
||||
--output_path ./inference \
|
||||
--class_dim 100 \
|
||||
--img_size 32
|
||||
-c ./ppcls/configs/quick_start/professional/ResNet50_vd_CIFAR100.yaml \
|
||||
-o Global.pretrained_model=output_CIFAR/ResNet50_vd/best_model
|
||||
```
|
||||
|
||||
其中，参数`--model`用于指定模型名称，`--pretrained_model`用于指定模型文件路径，`--output_path`用于指定转换后模型的存储路径。
|
||||
* 默认会在`inference`文件夹下生成`inference.pdiparams`、`inference.pdmodel`和`inference.pdiparams.info`文件。
|
||||
|
||||
* **注意**：
|
||||
* `--output_path`表示输出的inference模型文件夹路径，若`--output_path=./inference`，则会在`inference`文件夹下生成`inference.pdiparams`、`inference.pdmodel`和`inference.pdiparams.info`文件。
|
||||
使用预测引擎进行推理：
|
||||
|
||||
* 可以通过设置参数`--img_size`指定模型输入图像的`shape`，默认为`224`，表示图像尺寸为`224*224`，请根据实际情况修改。
|
||||
|
||||
上述命令将生成模型结构文件（`inference.pdmodel`）和模型权重文件（`inference.pdiparams`），然后可以使用预测引擎进行推理：
|
||||
进入deploy目录下：
|
||||
|
||||
```bash
|
||||
python3 tools/infer/predict.py \
|
||||
--image_file "./dataset/CIFAR100/test/0/0001.png" \
|
||||
--model_file "./inference/inference.pdmodel" \
|
||||
--params_file "./inference/inference.pdiparams" \
|
||||
--use_gpu=True \
|
||||
--use_tensorrt=False
|
||||
cd deploy
|
||||
```
|
||||
更改inference_cls.yaml文件，由于训练CIFAR100采用的分辨率是32x32，所以需要改变相关的分辨率，最终配置文件中的图像预处理如下：
|
||||
|
||||
```yaml
|
||||
PreProcess:
|
||||
transform_ops:
|
||||
- ResizeImage:
|
||||
resize_short: 36
|
||||
- CropImage:
|
||||
size: 32
|
||||
- NormalizeImage:
|
||||
scale: 0.00392157
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
```
|
||||
|
||||
### 5.2 多标签分类模型评估与预测
|
||||
|
||||
#### 5.2.1 多标签分类模型评估
|
||||
|
||||
训练好模型之后，可以通过以下命令实现对模型精度的评估。
|
||||
执行命令进行预测，由于默认class_id_map_file是ImageNet数据集的映射文件，所以此处需要置None。
|
||||
|
||||
```bash
|
||||
python3 tools/eval.py \
|
||||
-c ./configs/quick_start/ResNet50_vd_multilabel.yaml \
|
||||
-o pretrained_model="./output_NUS-WIDE-SCENE/ResNet50_vd/best_model/ppcls"
|
||||
```
|
||||
|
||||
评估指标采用mAP，验证集的mAP应该在0.57左右。
|
||||
|
||||
#### 5.2.2 多标签分类模型预测
|
||||
|
||||
```bash
|
||||
python3 tools/infer/infer.py \
|
||||
-i "./dataset/NUS-WIDE-SCENE/NUS-SCENE-dataset/images/0199_434752251.jpg" \
|
||||
--model ResNet50_vd \
|
||||
--pretrained_model "./output_NUS-WIDE-SCENE/ResNet50_vd/best_model/ppcls" \
|
||||
--use_gpu True \
|
||||
--multilabel True \
|
||||
--class_num 33
|
||||
python3 python/predict_cls.py \
|
||||
-c configs/inference_cls.yaml \
|
||||
-o Global.infer_imgs=../dataset/CIFAR100/test/0/0001.png \
|
||||
-o PostProcess.class_id_map_file=None
|
||||
```
|
||||
|
|
|
|||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "AlexNet"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.01
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.0001
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DPN107'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DPN131'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DPN68'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DPN92'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DPN98'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,77 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "DarkNet53"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 256, 256]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.0001
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 256
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 256
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DeiT_base_distilled_patch16_224'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 248
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DeiT_base_distilled_patch16_384'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 384, 384]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 384
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 426
|
||||
- CropImage:
|
||||
size: 384
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DeiT_base_patch16_224'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 248
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DeiT_base_patch16_384'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 384, 384]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 384
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 426
|
||||
- CropImage:
|
||||
size: 384
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DeiT_small_distilled_patch16_224'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 248
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DeiT_small_patch16_224'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 248
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DeiT_tiny_distilled_patch16_224'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 248
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DeiT_tiny_patch16_224'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 248
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DenseNet121'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DenseNet161'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DenseNet169'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DenseNet201'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'DenseNet264'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,86 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "EfficientNetB0"
|
||||
params:
|
||||
padding_type : "SAME"
|
||||
override_params:
|
||||
drop_connect_rate: 0.1
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
use_ema: True
|
||||
ema_decay: 0.9999
|
||||
use_aa: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'ExponentialWarmup'
|
||||
params:
|
||||
lr: 0.032
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'RMSProp'
|
||||
params:
|
||||
momentum: 0.9
|
||||
rho: 0.9
|
||||
epsilon: 0.001
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
TRAIN:
|
||||
batch_size: 512
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
interpolation: 2
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- AutoAugment:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 128
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
interpolation: 2
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'GhostNet_x0_5'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.8
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.0000400
|
||||
|
||||
TRAIN:
|
||||
batch_size: 2048
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'GhostNet_x1_0'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.4
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.0000400
|
||||
|
||||
TRAIN:
|
||||
batch_size: 1024
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,73 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'GhostNet_x1_3'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.4
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.0000400
|
||||
|
||||
TRAIN:
|
||||
batch_size: 1024
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- AutoAugment:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'HRNet_W18_C'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'HRNet_W30_C'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'HRNet_W32_C'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'HRNet_W40_C'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'HRNet_W44_C'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'HRNet_W48_C'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'HRNet_W64_C'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,69 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "GoogLeNet"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.01
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.0001
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,77 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'InceptionV3'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 299, 299]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.045
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 299
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 16
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 320
|
||||
- CropImage:
|
||||
size: 299
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,77 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'InceptionV4'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 299, 299]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.045
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00010
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 299
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 16
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 320
|
||||
- CropImage:
|
||||
size: 299
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
# just for finetune, the config for training on ImageNet is coming soon
|
||||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'MixNet_L'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
#just for finetune, the config for training on ImageNet is coming soon
|
||||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'MixNet_M'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
# just for finetune, the config for training on ImageNet is coming soon.
|
||||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'MixNet_S'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV1"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00003
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV1_x0_25"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00003
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV1_x0_5"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00003
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV1_x0_75"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00003
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV2"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 240
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.045
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00004
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV2_x0_25"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 240
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.045
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00003
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
ratio: [1.0, 1.0]
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV2_x0_5"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 240
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.045
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00003
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
ratio: [1.0, 1.0]
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV2_x0_75"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 240
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.045
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00004
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV2_x1_5"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 240
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.045
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00004
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV2_x2_0"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 240
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.045
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00004
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_large_x0_35"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 2.6
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00002
|
||||
|
||||
TRAIN:
|
||||
batch_size: 4096
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_large_x0_5"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 1.3
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00002
|
||||
|
||||
TRAIN:
|
||||
batch_size: 2048
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_large_x0_75"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 1.3
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00002
|
||||
|
||||
TRAIN:
|
||||
batch_size: 2048
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,76 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_large_x1_0"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 1.3
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00002
|
||||
|
||||
TRAIN:
|
||||
batch_size: 2048
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- AutoAugment:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 1024
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_large_x1_25"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.65
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00004
|
||||
|
||||
TRAIN:
|
||||
batch_size: 1024
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_small_x0_35"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 2.6
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
TRAIN:
|
||||
batch_size: 4096
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_small_x0_5"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 2.6
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00001
|
||||
|
||||
TRAIN:
|
||||
batch_size: 4096
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_small_x0_75"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 2.6
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00002
|
||||
|
||||
TRAIN:
|
||||
batch_size: 4096
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_small_x1_0"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 2.6
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00002
|
||||
|
||||
TRAIN:
|
||||
batch_size: 4096
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "MobileNetV3_small_x1_25"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
ls_epsilon: 0.1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 360
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 1.3
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.00002
|
||||
|
||||
TRAIN:
|
||||
batch_size: 2048
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
# just for finetune, the config for training on ImageNet is coming soon
|
||||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ReXNet_1_0'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
# just for finetune, the config for training on ImageNet is coming soon
|
||||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ReXNet_1_3'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
# just for finetune, the config for training on ImageNet is coming soon
|
||||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ReXNet_1_5'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
# just for finetune, the config for training on ImageNet is coming soon
|
||||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ReXNet_2_0'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
# just for finetune, the config for training on ImageNet is coming soon
|
||||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ReXNet_3_0'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,73 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RegNetX_4GF'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 100
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.4
|
||||
warmup_epoch: 5
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000050
|
||||
|
||||
TRAIN:
|
||||
batch_size: 512
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_A0'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_A1'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_A2'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B0'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B1'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B1g2'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B1g4'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B2'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B2g2'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B2g4'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B3'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B3g2'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,72 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'RepVGG_B3g4'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.001
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'Res2Net101_vd_26w_4s'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'Res2Net200_vd_26w_4s'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'Res2Net50_14w_8s'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'Res2Net50_26w_4s'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'Res2Net50_vd_26w_4s'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,76 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeSt101'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 300
|
||||
topk: 5
|
||||
image_shape: [3, 256, 256]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000070
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 256
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- AutoAugment:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- CutmixOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 288
|
||||
- CropImage:
|
||||
size: 256
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,76 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeSt50'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 300
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000070
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- AutoAugment:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- CutmixOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,76 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeSt50_fast_1s1x64d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 300
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000070
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- AutoAugment:
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- CutmixOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt101_32x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,89 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt101_32x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [4, 224, 224]
|
||||
|
||||
use_dali: True
|
||||
use_gpu: True
|
||||
data_format: "NCHW"
|
||||
image_channel: &image_channel 4
|
||||
image_shape: [*image_channel, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
# mixed precision training
|
||||
AMP:
|
||||
scale_loss: 128.0
|
||||
use_dynamic_loss_scaling: True
|
||||
use_pure_fp16: &use_pure_fp16 True
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
multi_precision: *use_pure_fp16
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
output_fp16: *use_pure_fp16
|
||||
channel_num: *image_channel
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt101_64x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000150
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt101_vd_32x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt101_vd_64x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt152_32x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt152_64x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000180
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt152_vd_32x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt152_vd_64x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Cosine'
|
||||
params:
|
||||
lr: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,74 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: 'ResNeXt50_32x4d'
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
use_mix: False
|
||||
ls_epsilon: -1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.000100
|
||||
|
||||
TRAIN:
|
||||
batch_size: 256
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "ResNeXt50_64x4d"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 120
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'Piecewise'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.0001
|
||||
|
||||
TRAIN:
|
||||
batch_size: 32
|
||||
num_workers: 8
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
|
|
@ -1,80 +0,0 @@
|
|||
mode: 'train'
|
||||
ARCHITECTURE:
|
||||
name: "ResNeXt50_vd_32x4d"
|
||||
|
||||
pretrained_model: ""
|
||||
model_save_dir: "./output/"
|
||||
classes_num: 1000
|
||||
total_images: 1281167
|
||||
save_interval: 1
|
||||
validate: True
|
||||
valid_interval: 1
|
||||
epochs: 200
|
||||
topk: 5
|
||||
image_shape: [3, 224, 224]
|
||||
use_mix: True
|
||||
ls_epsilon: 0.1
|
||||
|
||||
LEARNING_RATE:
|
||||
function: 'CosineWarmup'
|
||||
params:
|
||||
lr: 0.1
|
||||
decay_epochs: [30, 60, 90]
|
||||
gamma: 0.1
|
||||
|
||||
OPTIMIZER:
|
||||
function: 'Momentum'
|
||||
params:
|
||||
momentum: 0.9
|
||||
regularizer:
|
||||
function: 'L2'
|
||||
factor: 0.0001
|
||||
|
||||
TRAIN:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/train_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1./255.
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
mix:
|
||||
- MixupOperator:
|
||||
alpha: 0.2
|
||||
|
||||
|
||||
|
||||
VALID:
|
||||
batch_size: 64
|
||||
num_workers: 4
|
||||
file_list: "./dataset/ILSVRC2012/val_list.txt"
|
||||
data_dir: "./dataset/ILSVRC2012/"
|
||||
shuffle_seed: 0
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
|
||||
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue