[Docs] Add Chinese version of model_zoo (#5827)
* finish Common Setting * Finish ImageNet Pretrained Models * finish baseline * finishi Speed Benchmark * Finish Comparison with Detectron2 * Fix 中文 English 中文 and some mistakes * Modified docs/model_zoo.md * regenerate the tables * fix 中文数字中文 problem * fix mistakes * Fix markdownlint errorspull/6005/head
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# 模型库 |
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## 镜像地址 |
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从 MMDetection V2.0 起，我们只通过阿里云维护模型库。V1.x 版本的模型已经弃用。 |
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## 共同设置 |
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- 所有模型都是在 `coco_2017_train` 上训练，在 `coco_2017_val` 上测试。 |
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- 我们使用分布式训练。 |
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- 所有 pytorch-style 的 ImageNet 预训练主干网络来自 PyTorch 的模型库，caffe-style 的预训练主干网络来自 detectron2 最新开源的模型。 |
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- 为了与其他代码库公平比较，文档中所写的 GPU 内存是8个 GPU 的 `torch.cuda.max_memory_allocated()` 的最大值，此值通常小于 nvidia-smi 显示的值。 |
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- 我们以网络 foward 和后处理的时间加和作为推理时间，不包含数据加载时间。所有结果通过 [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) 脚本计算所得。该脚本会计算推理 2000 张图像的平均时间。 |
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## ImageNet 预训练模型 |
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通过 ImageNet 分类任务预训练的主干网络进行初始化是很常见的操作。所有预训练模型的链接都可以在 [open_mmlab](https://github.com/open-mmlab/mmcv/blob/master/mmcv/model_zoo/open_mmlab.json) 中找到。根据 `img_norm_cfg` 和原始权重，我们可以将所有 ImageNet 预训练模型分为以下几种情况： |
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- TorchVision：torchvision 模型权重，包含 ResNet50, ResNet101。`img_norm_cfg` 为 `dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)`。 |
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- Pycls：[pycls](https://github.com/facebookresearch/pycls) 模型权重，包含 RegNetX。`img_norm_cfg` 为 `dict( |
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mean=[103.530, 116.280, 123.675], std=[57.375, 57.12, 58.395], to_rgb=False)`。 |
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- MSRA styles：[MSRA](https://github.com/KaimingHe/deep-residual-networks) 模型权重，包含 ResNet50_Caffe，ResNet101_Caffe。`img_norm_cfg` 为 `dict( |
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mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)`。 |
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- Caffe2 styles：现阶段只包含 ResNext101_32x8d。`img_norm_cfg` 为 `dict(mean=[103.530, 116.280, 123.675], std=[57.375, 57.120, 58.395], to_rgb=False)`。 |
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- Other styles: SSD 的 `img_norm_cfg` 为 `dict(mean=[123.675, 116.28, 103.53], std=[1, 1, 1], to_rgb=True)`，YOLOv3 的 `img_norm_cfg` 为 `dict(mean=[0, 0, 0], std=[255., 255., 255.], to_rgb=True)`。 |
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MMdetection 常用到的主干网络细节如下表所示： |
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| 模型 | 来源 | 链接 | 描述 | |
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| ---------------- | ----------- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
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| ResNet50 | TorchVision | [torchvision 中的 ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth) | 来自 [torchvision 中的 ResNet-50](https://download.pytorch.org/models/resnet50-19c8e357.pth)。 | |
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| ResNet101 | TorchVision | [torchvision 中的 ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth) | 来自 [torchvision 中的 ResNet-101](https://download.pytorch.org/models/resnet101-5d3b4d8f.pth)。 | |
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| RegNetX | Pycls | [RegNetX_3.2gf](https://download.openmmlab.com/pretrain/third_party/regnetx_3.2gf-c2599b0f.pth)，[RegNetX_800mf](https://download.openmmlab.com/pretrain/third_party/regnetx_800mf-1f4be4c7.pth) 等 | 来自 [pycls](https://github.com/facebookresearch/pycls)。 | |
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| ResNet50_Caffe | MSRA | [MSRA 中的 ResNet-50](https://download.openmmlab.com/pretrain/third_party/resnet50_caffe-788b5fa3.pth) | 由 [Detectron2 中的 R-50.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-50.pkl) 转化的副本。原始权重文件来自 [MSRA 中的原始 ResNet-50](https://github.com/KaimingHe/deep-residual-networks)。 | |
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| ResNet101_Caffe | MSRA | [MSRA 中的 ResNet-101](https://download.openmmlab.com/pretrain/third_party/resnet101_caffe-3ad79236.pth) | 由 [Detectron2 中的 R-101.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/MSRA/R-101.pkl) 转化的副本。原始权重文件来自 [MSRA 中的原始 ResNet-101](https://github.com/KaimingHe/deep-residual-networks)。 | |
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| ResNext101_32x8d | Caffe2 | [Caffe2 ResNext101_32x8d](https://download.openmmlab.com/pretrain/third_party/resnext101_32x8d-1516f1aa.pth) | 由 [Detectron2 中的 X-101-32x8d.pkl](https://dl.fbaipublicfiles.com/detectron2/ImageNetPretrained/FAIR/X-101-32x8d.pkl) 转化的副本。原始 ResNeXt-101-32x8d 由 FB 使用 Caffe2 训练。 | |
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## Baselines |
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### RPN |
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请参考 [RPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/rpn)。 |
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### Faster R-CNN |
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请参考 [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn)。 |
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### Mask R-CNN |
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请参考 [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn)。 |
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### Fast R-CNN (使用提前计算的 proposals) |
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请参考 [Fast R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/fast_rcnn)。 |
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### RetinaNet |
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请参考 [RetinaNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet)。 |
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### Cascade R-CNN and Cascade Mask R-CNN |
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请参考 [Cascade R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/cascade_rcnn)。 |
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### Hybrid Task Cascade (HTC) |
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请参考 [HTC](https://github.com/open-mmlab/mmdetection/blob/master/configs/htc)。 |
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### SSD |
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请参考 [SSD](https://github.com/open-mmlab/mmdetection/blob/master/configs/ssd)。 |
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### Group Normalization (GN) |
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请参考 [Group Normalization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn)。 |
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### Weight Standardization |
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请参考 [Weight Standardization](https://github.com/open-mmlab/mmdetection/blob/master/configs/gn+ws)。 |
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### Deformable Convolution v2 |
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请参考 [Deformable Convolutional Networks](https://github.com/open-mmlab/mmdetection/blob/master/configs/dcn)。 |
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### CARAFE: Content-Aware ReAssembly of FEatures |
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请参考 [CARAFE](https://github.com/open-mmlab/mmdetection/blob/master/configs/carafe)。 |
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### Instaboost |
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请参考 [Instaboost](https://github.com/open-mmlab/mmdetection/blob/master/configs/instaboost)。 |
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### Libra R-CNN |
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请参考 [Libra R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/libra_rcnn)。 |
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### Guided Anchoring |
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请参考 [Guided Anchoring](https://github.com/open-mmlab/mmdetection/blob/master/configs/guided_anchoring)。 |
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### FCOS |
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请参考 [FCOS](https://github.com/open-mmlab/mmdetection/blob/master/configs/fcos)。 |
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### FoveaBox |
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请参考 [FoveaBox](https://github.com/open-mmlab/mmdetection/blob/master/configs/foveabox)。 |
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### RepPoints |
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请参考 [RepPoints](https://github.com/open-mmlab/mmdetection/blob/master/configs/reppoints)。 |
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### FreeAnchor |
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请参考 [FreeAnchor](https://github.com/open-mmlab/mmdetection/blob/master/configs/free_anchor)。 |
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### Grid R-CNN (plus) |
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请参考 [Grid R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/grid_rcnn)。 |
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### GHM |
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请参考 [GHM](https://github.com/open-mmlab/mmdetection/blob/master/configs/ghm)。 |
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### GCNet |
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请参考 [GCNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/gcnet)。 |
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### HRNet |
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请参考 [HRNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/hrnet)。 |
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### Mask Scoring R-CNN |
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请参考 [Mask Scoring R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/ms_rcnn)。 |
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### Train from Scratch |
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请参考 [Rethinking ImageNet Pre-training](https://github.com/open-mmlab/mmdetection/blob/master/configs/scratch)。 |
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### NAS-FPN |
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请参考 [NAS-FPN](https://github.com/open-mmlab/mmdetection/blob/master/configs/nas_fpn)。 |
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### ATSS |
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请参考 [ATSS](https://github.com/open-mmlab/mmdetection/blob/master/configs/atss)。 |
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### FSAF |
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请参考 [FSAF](https://github.com/open-mmlab/mmdetection/blob/master/configs/fsaf)。 |
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### RegNetX |
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请参考 [RegNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet)。 |
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### Res2Net |
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请参考 [Res2Net](https://github.com/open-mmlab/mmdetection/blob/master/configs/res2net)。 |
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### GRoIE |
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请参考 [GRoIE](https://github.com/open-mmlab/mmdetection/blob/master/configs/groie)。 |
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### Dynamic R-CNN |
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请参考 [Dynamic R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/dynamic_rcnn)。 |
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### PointRend |
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请参考 [PointRend](https://github.com/open-mmlab/mmdetection/blob/master/configs/point_rend)。 |
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### DetectoRS |
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请参考 [DetectoRS](https://github.com/open-mmlab/mmdetection/blob/master/configs/detectors)。 |
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### Generalized Focal Loss |
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请参考 [Generalized Focal Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/gfl)。 |
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### CornerNet |
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请参考 [CornerNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/cornernet)。 |
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### YOLOv3 |
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请参考 [YOLOv3](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolo)。 |
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### PAA |
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请参考 [PAA](https://github.com/open-mmlab/mmdetection/blob/master/configs/paa)。 |
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### SABL |
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请参考 [SABL](https://github.com/open-mmlab/mmdetection/blob/master/configs/sabl)。 |
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### CentripetalNet |
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请参考 [CentripetalNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centripetalnet)。 |
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### ResNeSt |
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请参考 [ResNeSt](https://github.com/open-mmlab/mmdetection/blob/master/configs/resnest)。 |
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### DETR |
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请参考 [DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/detr)。 |
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### Deformable DETR |
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请参考 [Deformable DETR](https://github.com/open-mmlab/mmdetection/blob/master/configs/deformable_detr)。 |
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### AutoAssign |
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请参考 [AutoAssign](https://github.com/open-mmlab/mmdetection/blob/master/configs/autoassign)。 |
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### YOLOF |
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请参考 [YOLOF](https://github.com/open-mmlab/mmdetection/blob/master/configs/yolof)。 |
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### Seesaw Loss |
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请参考 [Seesaw Loss](https://github.com/open-mmlab/mmdetection/blob/master/configs/seesaw_loss)。 |
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### CenterNet |
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请参考 [CenterNet](https://github.com/open-mmlab/mmdetection/blob/master/configs/centernet)。 |
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### Other datasets |
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我们还在 [PASCAL VOC](https://github.com/open-mmlab/mmdetection/blob/master/configs/pascal_voc)，[Cityscapes](https://github.com/open-mmlab/mmdetection/blob/master/configs/cityscapes) 和 [WIDER FACE](https://github.com/open-mmlab/mmdetection/blob/master/configs/wider_face) 上对一些方法进行了基准测试。 |
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### Pre-trained Models |
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我们还通过多尺度训练和更长的训练策略来训练用 ResNet-50 和 [RegNetX-3.2G](https://github.com/open-mmlab/mmdetection/blob/master/configs/regnet) 作为主干网络的 [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn) 和 [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn)。这些模型可以作为下游任务的预训练模型。 |
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## 速度基准 |
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### 训练速度基准 |
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我们提供 [analyze_logs.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/analyze_logs.py) 来得到训练中每一次迭代的平均时间。示例请参考 [Log Analysis](https://mmdetection.readthedocs.io/en/latest/useful_tools.html#log-analysis)。 |
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我们与其他流行框架的 Mask R-CNN 训练速度进行比较（数据是从 [detectron2](https://github.com/facebookresearch/detectron2/blob/master/docs/notes/benchmarks.md/) 复制而来）。在 mmdetection 中，我们使用 [mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_poly_1x_coco_v1.py) 进行基准测试。它与 detectron2 的 [mask_rcnn_R_50_FPN_noaug_1x.yaml](https://github.com/facebookresearch/detectron2/blob/master/configs/Detectron1-Comparisons/mask_rcnn_R_50_FPN_noaug_1x.yaml) 设置完全一样。同时，我们还提供了[模型权重](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_compare_20200518-10127928.pth)和[训练 log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug/mask_rcnn_r50_caffe_fpn_poly_1x_coco_no_aug_20200518_105755.log.json) 作为参考。为了跳过 GPU 预热时间，吞吐量按照100-500次迭代之间的平均吞吐量来计算。 |
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| 框架 | 吞吐量 (img/s) | |
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| -------------------------------------------------------------------------------------- | ------------------ | |
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| [Detectron2](https://github.com/facebookresearch/detectron2) | 62 | |
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| [MMDetection](https://github.com/open-mmlab/mmdetection) | 61 | |
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| [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark/) | 53 | |
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| [tensorpack](https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN) | 50 | |
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| [simpledet](https://github.com/TuSimple/simpledet/) | 39 | |
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| [Detectron](https://github.com/facebookresearch/Detectron) | 19 | |
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| [matterport/Mask_RCNN](https://github.com/matterport/Mask_RCNN/) | 14 | |
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### 推理时间基准 |
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我们提供 [benchmark.py](https://github.com/open-mmlab/mmdetection/blob/master/tools/analysis_tools/benchmark.py) 对推理时间进行基准测试。此脚本将推理 2000 张图片并计算忽略前 5 次推理的平均推理时间。可以通过设置 `LOG-INTERVAL` 来改变 log 输出间隔（默认为 50）。 |
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```shell |
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python toools/benchmark.py ${CONFIG} ${CHECKPOINT} [--log-interval $[LOG-INTERVAL]] [--fuse-conv-bn] |
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``` |
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模型库中，所有模型在基准测量推理时间时都没设置 `fuse-conv-bn`, 此设置可以使推理时间更短。 |
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## 与 Detectron2 对比 |
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我们在速度和精度方面对 mmdetection 和 [Detectron2](https://github.com/facebookresearch/detectron2.git) 进行对比。对比所使用的 detectron2 的 commit id 为 [185c27e](https://github.com/facebookresearch/detectron2/tree/185c27e4b4d2d4c68b5627b3765420c6d7f5a659)(30/4/2020)。 |
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为了公平对比，我们所有的实验都在同一机器下进行。 |
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### 硬件 |
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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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### 软件环境 |
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- Python 3.7 |
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- PyTorch 1.4 |
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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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### 精度 |
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| 模型 | 训练策略 | Detectron2 | mmdetection | 下载 | |
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| -------------------------------------------------------------------------------------------------------------------------------------- | -------- | -------------------------------------------------------------------------------------------------------------------------------------- | ----------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | |
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| [Faster R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/faster_rcnn/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [37.9](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/faster_rcnn_R_50_FPN_1x.yaml) | 38.0 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco-5324cff8.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco/faster_rcnn_r50_caffe_fpn_mstrain_1x_coco_20200429_234554.log.json) | |
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| [Mask R-CNN](https://github.com/open-mmlab/mmdetection/blob/master/configs/mask_rcnn/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco.py) | 1x | [38.6 & 35.2](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_1x.yaml) | 38.8 & 35.4 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco-dbecf295.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco/mask_rcnn_r50_caffe_fpn_mstrain-poly_1x_coco_20200430_054239.log.json) | |
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| [Retinanet](https://github.com/open-mmlab/mmdetection/blob/master/configs/retinanet/retinanet_r50_caffe_fpn_mstrain_1x_coco.py) | 1x | [36.5](https://github.com/facebookresearch/detectron2/blob/master/configs/COCO-Detection/retinanet_R_50_FPN_1x.yaml) | 37.0 | [model](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco-586977a0.pth) | [log](https://download.openmmlab.com/mmdetection/v2.0/benchmark/retinanet_r50_caffe_fpn_mstrain_1x_coco/retinanet_r50_caffe_fpn_mstrain_1x_coco_20200430_014748.log.json) | |
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|
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### 训练速度 |
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|
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训练速度使用 s/iter 来度量。结果越低越好。 |
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|
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| 模型 | Detectron2 | mmdetection | |
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| ------------ | ---------- | ----------- | |
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| Faster R-CNN | 0.210 | 0.216 | |
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| Mask R-CNN | 0.261 | 0.265 | |
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| Retinanet | 0.200 | 0.205 | |
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|
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### 推理速度 |
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|
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推理速度通过单张 GPU 下的 fps(img/s) 来度量，越高越好。 |
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为了与 Detectron2 保持一致，我们所写的推理时间除去了数据加载时间。 |
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对于 Mask RCNN，我们去除了后处理中 RLE 编码的时间。 |
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我们在括号中给出了官方给出的速度。由于硬件差异，官方给出的速度会比我们所测试得到的速度快一些。 |
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|
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| 模型 | Detectron2 | mmdetection | |
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| ------------ | ----------- | ----------- | |
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| Faster R-CNN | 25.6 (26.3) | 22.2 | |
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| Mask R-CNN | 22.5 (23.3) | 19.6 | |
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| Retinanet | 17.8 (18.2) | 20.6 | |
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|
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### 训练内存 |
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|
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| 模型 | Detectron2 | mmdetection | |
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| ------------ | ---------- | ----------- | |
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| Faster R-CNN | 3.0 | 3.8 | |
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| Mask R-CNN | 3.4 | 3.9 | |
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| Retinanet | 3.9 | 3.4 | |
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|
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Reference in new issue