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* Correct get_started.md * Correct dataset_prepare.md * Correct model_zoo.md * Correct train.md * Correct inference.md * Correct config.md * Correct customize_datasets.md * Correct data_pipeline.md * Correct customize_models.md * Correct training_tricks.md * Correct customize_runtime.md * Correct useful_tools.md and translate "model serving" * Fix typos * fix lint * Modify the content of useful_tools.md to meet the requirements, and modify some of the content by referring to the Chinese documentation of mmcls. * Modify the use_tools.md file based on feedback. Adjusted some translations according to "English-Chinese terminology comparison". * Modify get_start.md . Adjusted some translations according to "English-Chinese terminology comparison". * Modify dataset_prepare.md. * Modify the English version and the Chinese version of model_zoo.md. Adjusted some translations according to "English-Chinese terminology comparison". * Modify train.md. Adjusted some translations according to "English-Chinese terminology comparison". * Modify inference.md. Adjusted some translations according to "English-Chinese terminology comparison". * Modify config.md. Adjusted some translations according to "English-Chinese terminology comparison". * Modify customize_datasets.md. * Modify data_pipeline.md. Adjusted some translations according to "English-Chinese terminology comparison". The main corrected term is: pipeline. * Modify customize_models.md. * Modify training_tricks.md. * Modify customize_runtime.md. Adjusted some translations according to "English-Chinese terminology comparison". * fix full point usage in items * fix typo * fix typo * fix typo * fix typo * Update useful_tools.md Co-authored-by: Junjun2016 <hejunjun@sjtu.edu.cn> Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>
180 lines
6.5 KiB
Markdown
180 lines
6.5 KiB
Markdown
# Benchmark and Model Zoo
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## Common settings
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* We use distributed training with 4 GPUs by default.
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* All pytorch-style pretrained backbones on ImageNet are train by ourselves, with the same procedure in the [paper](https://arxiv.org/pdf/1812.01187.pdf).
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Our ResNet style backbone are based on ResNetV1c variant, where the 7x7 conv in the input stem is replaced with three 3x3 convs.
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* For the consistency across different hardwares, we report the GPU memory as the maximum value of `torch.cuda.max_memory_allocated()` for all 4 GPUs with `torch.backends.cudnn.benchmark=False`.
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Note that this value is usually less than what `nvidia-smi` shows.
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* We report the inference time as the total time of network forwarding and post-processing, excluding the data loading time.
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Results are obtained with the script `tools/benchmark.py` which computes the average time on 200 images with `torch.backends.cudnn.benchmark=False`.
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* There are two inference modes in this framework.
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* `slide` mode: The `test_cfg` will be like `dict(mode='slide', crop_size=(769, 769), stride=(513, 513))`.
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In this mode, multiple patches will be cropped from input image, passed into network individually.
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The crop size and stride between patches are specified by `crop_size` and `stride`.
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The overlapping area will be merged by average
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* `whole` mode: The `test_cfg` will be like `dict(mode='whole')`.
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In this mode, the whole imaged will be passed into network directly.
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By default, we use `slide` inference for 769x769 trained model, `whole` inference for the rest.
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* For input size of 8x+1 (e.g. 769), `align_corner=True` is adopted as a traditional practice.
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Otherwise, for input size of 8x (e.g. 512, 1024), `align_corner=False` is adopted.
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## Baselines
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### FCN
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Please refer to [FCN](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fcn) for details.
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### PSPNet
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Please refer to [PSPNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/pspnet) for details.
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### DeepLabV3
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Please refer to [DeepLabV3](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3) for details.
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### PSANet
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Please refer to [PSANet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/psanet) for details.
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### DeepLabV3+
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Please refer to [DeepLabV3+](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/deeplabv3plus) for details.
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### UPerNet
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Please refer to [UPerNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/upernet) for details.
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### NonLocal Net
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Please refer to [NonLocal Net](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/nonlocal_net) for details.
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### EncNet
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Please refer to [EncNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/encnet) for details.
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### CCNet
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Please refer to [CCNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ccnet) for details.
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### DANet
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Please refer to [DANet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/danet) for details.
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### APCNet
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Please refer to [APCNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/apcnet) for details.
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### HRNet
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Please refer to [HRNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/hrnet) for details.
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### GCNet
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Please refer to [GCNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/gcnet) for details.
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### DMNet
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Please refer to [DMNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dmnet) for details.
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### ANN
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Please refer to [ANN](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ann) for details.
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### OCRNet
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Please refer to [OCRNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/ocrnet) for details.
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### Fast-SCNN
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Please refer to [Fast-SCNN](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastscnn) for details.
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### ResNeSt
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Please refer to [ResNeSt](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/resnest) for details.
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### Semantic FPN
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Please refer to [Semantic FPN](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/sem_fpn) for details.
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### PointRend
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Please refer to [PointRend](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/point_rend) for details.
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### MobileNetV2
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Please refer to [MobileNetV2](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v2) for details.
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### MobileNetV3
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Please refer to [MobileNetV3](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/mobilenet_v3) for details.
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### EMANet
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Please refer to [EMANet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/emanet) for details.
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### DNLNet
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Please refer to [DNLNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/dnlnet) for details.
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### CGNet
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Please refer to [CGNet](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/cgnet) for details.
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### Mixed Precision (FP16) Training
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Please refer [Mixed Precision (FP16) Training](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fp16) for details.
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### U-Net
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Please refer to [U-Net](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/unet/README.md) for details.
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### ViT
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Please refer to [ViT](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/vit/README.md) for details.
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### Swin
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Please refer to [Swin](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/swin/README.md) for details.
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### SETR
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Please refer to [SETR](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/setr/README.md) for details.
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## Speed benchmark
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### Hardware
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* 8 NVIDIA Tesla V100 (32G) GPUs
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* Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz
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### Software environment
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* Python 3.7
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* PyTorch 1.5
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* CUDA 10.1
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* CUDNN 7.6.03
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* NCCL 2.4.08
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### Training speed
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For fair comparison, we benchmark all implementations with ResNet-101V1c.
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The input size is fixed to 1024x512 with batch size 2.
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The training speed is reported as followed, in terms of second per iter (s/iter). The lower, the better.
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| Implementation | PSPNet (s/iter) | DeepLabV3+ (s/iter) |
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| [MMSegmentation](https://github.com/open-mmlab/mmsegmentation) | **0.83** | **0.85** |
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| [SegmenTron](https://github.com/LikeLy-Journey/SegmenTron) | 0.84 | 0.85 |
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| [CASILVision](https://github.com/CSAILVision/semantic-segmentation-pytorch) | 1.15 | N/A |
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| [vedaseg](https://github.com/Media-Smart/vedaseg) | 0.95 | 1.25 |
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Note: The output stride of DeepLabV3+ is 8.
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