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README_zh-CN.md
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@ -11,9 +11,9 @@
## Introduction
MMClassification是一款基于PyTorch的开源图像分类工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一
MMClassification 是一款基于 PyTorch 的开源图像分类工具箱，是 [OpenMMLab](https://openmmlab.com/) 项目的成员之一
参考文档: https://mmclassification.readthedocs.io/en/latest/
参考文档：https://mmclassification.readthedocs.io/en/latest/
![demo](https://user-images.githubusercontent.com/9102141/87268895-3e0d0780-c4fe-11ea-849e-6140b7e0d4de.gif)
@ -36,7 +36,7 @@ MMClassification是一款基于PyTorch的开源图像分类工具箱，是 [Open
## 基准测试及模型库
相关结果和模型可在 [model zoo](docs_zh-CN/model_zoo.md) 中获得
相关结果和模型可在 [model zoo](docs/model_zoo.md) 中获得
支持的主干网络：
@ -72,7 +72,7 @@ MMClassification 是一款由不同学校和公司共同贡献的开源项目。
- [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
- [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 检测工具箱与测试基准
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用3D目标检测平台
- [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
- [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱与测试基准
- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱与测试基准
- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台

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@ -72,10 +72,10 @@ python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--metrics ${METRICS}] [-
可选参数：
- `RESULT_FILE`：输出结果的文件名。如果未指定，结果将不会保存到文件中。支持json, yaml, pickle格式。
- `METRICS`：数据集测试指标，如准确率(accuracy), 精确率(precision), 召回率(recall)等
- `RESULT_FILE`：输出结果的文件名。如果未指定，结果将不会保存到文件中。支持 json, yaml, pickle 格式。
- `METRICS`：数据集测试指标，如准确率 (accuracy), 精确率 (precision), 召回率 (recall) 等
例子:
例子：
假定用户将下载的模型权重文件放置在 `checkpoints/` 目录下。
@ -94,7 +94,7 @@ MMClassification 使用 `MMDistributedDataParallel` 进行分布式训练，使
默认情况下，MMClassification 在每个周期后会在验证集上评估模型，可以通过在训练配置中修改 `interval` 参数来更改评估间隔
```python
evaluation = dict(interval=12) # 每进行12轮训练后评估一次模型
evaluation = dict(interval=12) # 每进行 12 轮训练后评估一次模型
```
### 使用单个 GPU 进行训练
@ -113,7 +113,7 @@ python tools/train.py ${CONFIG_FILE} [optional arguments]
可选参数为：
- `--no-validate` (**不建议**): 默认情况下，程序将会在训练期间的每 k (默认为 1) 个周期进行一次验证。要禁用这一功能，使用 `--no-validate`
- `--no-validate` (**不建议**): 默认情况下，程序将会在训练期间的每 k （默认为 1) 个周期进行一次验证。要禁用这一功能，使用 `--no-validate`
- `--work-dir ${WORK_DIR}`：覆盖配置文件中指定的工作目录。
- `--resume-from ${CHECKPOINT_FILE}`：从以前的模型权重文件恢复训练。
@ -214,7 +214,7 @@ python tools/publish_model.py work_dirs/resnet50/latest.pth imagenet_resnet50_20
## 详细教程
目前, MMClassification 提供以下几种更详细的教程：
目前，MMClassification 提供以下几种更详细的教程：
- [如何微调模型](tutorials/finetune.md)
- [如何增加新数据集](tutorials/new_dataset.md)

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@ -22,14 +22,14 @@ MMClassification 和 MMCV 的适配关系如下，请安装正确版本的 MMCV
### 安装 MMClassification 步骤
a. 创建conda虚拟环境，并激活
a. 创建 conda 虚拟环境，并激活
```shell
conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab
```
b. 按照 [官方指南](https://pytorch.org/) 安装PyTorch和TorchVision，如:
b. 按照 [官方指南](https://pytorch.org/) 安装 PyTorch 和 TorchVision，如：
```shell
conda install pytorch torchvision -c pytorch
@ -69,7 +69,7 @@ pip install -e . # or "python setup.py develop"
提示：
1. 按照以上步骤，MMClassification 是以 `dev` 模式安装的，任何本地的代码修改都可以直接生效，无需重新安装（除非提交了一些commit，并且希望提升版本号）
1. 按照以上步骤，MMClassification 是以 `dev` 模式安装的，任何本地的代码修改都可以直接生效，无需重新安装（除非提交了一些 commit，并且希望提升版本号）
2. 如果希望使用 `opencv-python-headless` 而不是 `opencv-python`，可以在安装 [mmcv](https://github.com/open-mmlab/mmcv) 之前提前安装。

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# Model Zoo
## ImageNet
ImageNet has multiple versions, but the most commonly used one is [ILSVRC 2012](http://www.image-net.org/challenges/LSVRC/2012/).
The ResNet family models below are trained by standard data augmentations, i.e., RandomResizedCrop, RandomHorizontalFlip and Normalize.
| Model | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Config | Download |
|:---------------------:|:---------:|:--------:|:---------:|:---------:|:---------:|:--------:|
| VGG-11 | 132.86 | 7.63 | 68.75 | 88.87 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_batch256_imagenet_20210208-4271cd6c.log.json) |
| VGG-13 | 133.05 | 11.34 | 70.02 | 89.46 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_batch256_imagenet_20210208-4d1d6080.log.json) |
| VGG-16 | 138.36 | 15.5 | 71.62 | 90.49 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_batch256_imagenet_20210208-db26f1a5.log.json) |
| VGG-19 | 143.67 | 19.67 | 72.41 | 90.80 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
| VGG-11-BN | 132.87 | 7.64 | 70.75 | 90.12 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg11bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg11_bn_batch256_imagenet_20210207-f244902c.log.json) |
| VGG-13-BN | 133.05 | 11.36 | 72.15 | 90.71 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg13bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg13_bn_batch256_imagenet_20210207-1a8b7864.log.json) |
| VGG-16-BN | 138.37 | 15.53 | 73.72 | 91.68 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg16_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg16_bn_batch256_imagenet_20210208-7e55cd29.log.json) |
| VGG-19-BN | 143.68 | 19.7 | 74.70 | 92.24 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/vgg/vgg19bn_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.pth) | [log](https://download.openmmlab.com/mmclassification/v0/vgg/vgg19_bn_batch256_imagenet_20210208-da620c4f.log.json)|
| ResNet-18 | 11.69 | 1.82 | 70.07 | 89.44 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_batch256_imagenet_20200708-34ab8f90.log.json) |
| ResNet-34 | 21.8 | 3.68 | 73.85 | 91.53 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_batch256_imagenet_20200708-32ffb4f7.log.json) |
| ResNet-50 | 25.56 | 4.12 | 76.55 | 93.15 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_batch256_imagenet_20200708-cfb998bf.log.json) |
| ResNet-101 | 44.55 | 7.85 | 78.18 | 94.03 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_batch256_imagenet_20200708-753f3608.log.json) |
| ResNeSt-50 | 27.48 | 5.41 | 81.13 | 95.59 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest50_imagenet_converted-1ebf0afe.pth) | [log]() |
| ResNeSt-101 | 48.28 | 10.27 | 82.32 | 96.24 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest101_imagenet_converted-032caa52.pth) | [log]() |
| ResNeSt-200 | 70.2 | 17.53 | 82.41 | 96.22 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest200_imagenet_converted-581a60f2.pth) | [log]() |
| ResNeSt-269 | 110.93 | 22.58 | 82.70 | 96.28 | | [model](https://download.openmmlab.com/mmclassification/v0/resnest/resnest269_imagenet_converted-59930960.pth) | [log]() |
| ResNet-152 | 60.19 | 11.58 | 78.63 | 94.16 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_batch256_imagenet_20200708-ec25b1f9.log.json) |
| ResNetV1D-50 | 25.58 | 4.36 | 77.4 | 93.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d50_batch256_imagenet_20200708-1ad0ce94.log.json) |
| ResNetV1D-101 | 44.57 | 8.09 | 78.85 | 94.38 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d101_batch256_imagenet_20200708-9cb302ef.log.json) |
| ResNetV1D-152 | 60.21 | 11.82 | 79.35 | 94.61 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnetv1d152_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnetv1d152_batch256_imagenet_20200708-e79cb6a2.log.json) |
| ResNeXt-32x4d-50 | 25.03 | 4.27 | 77.92 | 93.74 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext50_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext50_32x4d_batch256_imagenet_20200708-c07adbb7.log.json) |
| ResNeXt-32x4d-101 | 44.18 | 8.03 | 78.7 | 94.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_batch256_imagenet_20200708-87f2d1c9.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x4d_batch256_imagenet_20200708-87f2d1c9.log.json) |
| ResNeXt-32x8d-101 | 88.79 | 16.5 | 79.22 | 94.52 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext101_32x8d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_batch256_imagenet_20200708-1ec34aa7.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext101_32x8d_batch256_imagenet_20200708-1ec34aa7.log.json) |
| ResNeXt-32x4d-152 | 59.95 | 11.8 | 79.06 | 94.47 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnext/resnext152_32x4d_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_batch256_imagenet_20200708-aab5034c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnext/resnext152_32x4d_batch256_imagenet_20200708-aab5034c.log.json) |
| SE-ResNet-50 | 28.09 | 4.13 | 77.74 | 93.84 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet50_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200804-ae206104.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet50_batch256_imagenet_20200708-657b3c36.log.json) |
| SE-ResNet-101 | 49.33 | 7.86 | 78.26 | 94.07 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/seresnet/seresnet101_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200804-ba5b51d4.pth) | [log](https://download.openmmlab.com/mmclassification/v0/se-resnet/se-resnet101_batch256_imagenet_20200708-038a4d04.log.json) |
| ShuffleNetV1 1.0x (group=3) | 1.87 | 0.146 | 68.13 | 87.81 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v1/shufflenet_v1_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v1/shufflenet_v1_batch1024_imagenet_20200804-5d6cec73.log.json) |
| ShuffleNetV2 1.0x | 2.28 | 0.149 | 69.55 | 88.92 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/shufflenet_v2/shufflenet_v2_1x_b64x16_linearlr_bn_nowd_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200812-5bf4721e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/shufflenet_v2/shufflenet_v2_batch1024_imagenet_20200804-8860eec9.log.json) |
| MobileNet V2 | 3.5 | 0.319 | 71.86 | 90.42 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/mobilenet_v2/mobilenet_v2_b32x8_imagenet.py) | [model](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.pth) | [log](https://download.openmmlab.com/mmclassification/v0/mobilenet_v2/mobilenet_v2_batch256_imagenet_20200708-3b2dc3af.log.json) |
Models with * are converted from other repos, others are trained by ourselves.
## CIFAR10
| Model | Params(M) | Flops(G) | Top-1 (%) | Config | Download |
|:---------------------:|:---------:|:--------:|:---------:|:--------:|:--------:|
| ResNet-18-b16x8 | 11.17 | 0.56 | 94.72 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet18_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20200823-f906fa4e.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet18_b16x8_cifar10_20200823-f906fa4e.log.json) |
| ResNet-34-b16x8 | 21.28 | 1.16 | 95.34 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet34_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20200823-52d5d832.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet34_b16x8_cifar10_20200823-52d5d832.log.json) |
| ResNet-50-b16x8 | 23.52 | 1.31 | 95.36 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet50_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20200823-882aa7b1.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet50_b16x8_cifar10_20200823-882aa7b1.log.json) |
| ResNet-101-b16x8 | 42.51 | 2.52 | 95.66 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet101_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20200823-d9501bbc.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet101_b16x8_cifar10_20200823-d9501bbc.log.json) |
| ResNet-152-b16x8 | 58.16 | 3.74 | 95.96 | [config](https://github.com/open-mmlab/mmclassification/blob/master/configs/resnet/resnet152_b16x8_cifar10.py) | [model](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20200823-ad4d5d0c.pth) | [log](https://download.openmmlab.com/mmclassification/v0/resnet/resnet152_b16x8_cifar10_20200823-ad4d5d0c.log.json) |