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Commit 11e9c74c authored by Cao Yuhang's avatar Cao Yuhang Committed by Kai Chen
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Update the comparison with other codebases in the model zoo (#573) 

* compared with maskrcnn-benchmark instead detectron.pytorch, delete some outdated sentences

* add memory data

* Update MODEL_ZOO.md
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MODEL_ZOO.md
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...@@ -27,13 +27,8 @@ You can replace `https://s3.ap-northeast-2.amazonaws.com/open-mmlab` with `https ...@@ -27,13 +27,8 @@ You can replace `https://s3.ap-northeast-2.amazonaws.com/open-mmlab` with `https
- We use distributed training and BN layer stats are fixed. - We use distributed training and BN layer stats are fixed.
- We adopt the same training schedules as Detectron. 1x indicates 12 epochs and 2x indicates 24 epochs, which corresponds to slightly less iterations than Detectron and the difference can be ignored. - We adopt the same training schedules as Detectron. 1x indicates 12 epochs and 2x indicates 24 epochs, which corresponds to slightly less iterations than Detectron and the difference can be ignored.
- All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo. - All pytorch-style pretrained backbones on ImageNet are from PyTorch model zoo.
- We report the training GPU memory as the maximum value of `torch.cuda.max_memory_cached()` - For fair comparison with other codebases, we report the GPU memory as the maximum value of `torch.cuda.max_memory_allocated()` for all 8 GPUs. Note that this value is usually less than what `nvidia-smi` shows.
for all 8 GPUs. Note that this value is usually less than what `nvidia-smi` shows, but - We report the inference time as the overall time including data loading, network forwarding and post processing.
closer to the actual requirements.
- We report the inference time as the overall time including data loading,
network forwarding and post processing.
- The training memory and time of 2x schedule is simply copied from 1x.
It should be very close to the actual memory and time.
## Baselines ## Baselines
...@@ -46,14 +41,14 @@ More models with different backbones will be added to the model zoo. ...@@ -46,14 +41,14 @@ More models with different backbones will be added to the model zoo.
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN | caffe | 1x | 3.3 | 0.253 | 16.9 | 58.2 | - | | R-50-FPN | caffe | 1x | 3.3 | 0.253 | 16.9 | 58.2 | - |
| R-50-FPN | pytorch | 1x | 3.5 | 0.276 | 17.7 | 57.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_1x_20181010-4a9c0712.pth) | | R-50-FPN | pytorch | 1x | 3.5 | 0.276 | 17.7 | 57.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_1x_20181010-4a9c0712.pth) |
| R-50-FPN | pytorch | 2x | | | | 57.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_2x_20181010-88a4a471.pth) | | R-50-FPN | pytorch | 2x | - | - | - | 57.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_2x_20181010-88a4a471.pth) |
| R-101-FPN | caffe | 1x | 5.2 | 0.379 | 13.9 | 59.4 | - | | R-101-FPN | caffe | 1x | 5.2 | 0.379 | 13.9 | 59.4 | - |
| R-101-FPN | pytorch | 1x | 5.4 | 0.396 | 14.4 | 58.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_1x_20181129-f50da4bd.pth) | | R-101-FPN | pytorch | 1x | 5.4 | 0.396 | 14.4 | 58.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_1x_20181129-f50da4bd.pth) |
| R-101-FPN | pytorch | 2x | | | | 59.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_2x_20181129-e42c6c9a.pth) | | R-101-FPN | pytorch | 2x | - | - | - | 59.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r101_fpn_2x_20181129-e42c6c9a.pth) |
| X-101-32x4d-FPN | pytorch |1x | 6.6 | 0.589 | 11.8 | 59.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_1x_20181218-7e379d26.pth) | X-101-32x4d-FPN | pytorch |1x | 6.6 | 0.589 | 11.8 | 59.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_1x_20181218-7e379d26.pth)
| X-101-32x4d-FPN | pytorch |2x | | | | 59.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_2x_20181218-0510af40.pth) | X-101-32x4d-FPN | pytorch |2x | - | - | - | 59.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_32x4d_fpn_2x_20181218-0510af40.pth)
| X-101-64x4d-FPN | pytorch |1x | 9.5 | 0.955 | 8.3 | 59.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_1x_20181218-c1a24f1f.pth) | X-101-64x4d-FPN | pytorch |1x | 9.5 | 0.955 | 8.3 | 59.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_1x_20181218-c1a24f1f.pth)
| X-101-64x4d-FPN | pytorch |2x | | | | 60.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_2x_20181218-c22bdd70.pth) | X-101-64x4d-FPN | pytorch |2x | - | - | - | 60.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_x101_64x4d_fpn_2x_20181218-c22bdd70.pth)
### Faster R-CNN ### Faster R-CNN
...@@ -61,14 +56,14 @@ More models with different backbones will be added to the model zoo. ...@@ -61,14 +56,14 @@ More models with different backbones will be added to the model zoo.
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN | caffe | 1x | 3.6 | 0.333 | 12.9 | 36.7 | - | | R-50-FPN | caffe | 1x | 3.6 | 0.333 | 12.9 | 36.7 | - |
| R-50-FPN | pytorch | 1x | 3.8 | 0.353 | 12.5 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth) | | R-50-FPN | pytorch | 1x | 3.8 | 0.353 | 12.5 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010-3d1b3351.pth) |
| R-50-FPN | pytorch | 2x | | | | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_2x_20181010-443129e1.pth) | | R-50-FPN | pytorch | 2x | - | - | - | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_2x_20181010-443129e1.pth) |
| R-101-FPN | caffe | 1x | 5.5 | 0.465 | 10.7 | 38.8 | - | | R-101-FPN | caffe | 1x | 5.5 | 0.465 | 10.7 | 38.8 | - |
| R-101-FPN | pytorch | 1x | 5.7 | 0.474 | 10.8 | 38.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_1x_20181129-d1468807.pth) | | R-101-FPN | pytorch | 1x | 5.7 | 0.474 | 10.8 | 38.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_1x_20181129-d1468807.pth) |
| R-101-FPN | pytorch | 2x | | | | 39.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_2x_20181129-73e7ade7.pth) | | R-101-FPN | pytorch | 2x | - | - | - | 39.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r101_fpn_2x_20181129-73e7ade7.pth) |
| X-101-32x4d-FPN | pytorch | 1x| 6.9 | 0.672 | 9.3 | 40.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_1x_20181218-ad81c133.pth) | X-101-32x4d-FPN | pytorch | 1x| 6.9 | 0.672 | 9.3 | 40.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_1x_20181218-ad81c133.pth)
| X-101-32x4d-FPN | pytorch | 2x| | | | 40.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_2x_20181218-0ed58946.pth) | X-101-32x4d-FPN | pytorch | 2x| - | - | - | 40.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_32x4d_fpn_2x_20181218-0ed58946.pth)
| X-101-64x4d-FPN | pytorch | 1x| 9.8 | 1.040 | 7.1 | 41.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_1x_20181218-c9c69c8f.pth) | X-101-64x4d-FPN | pytorch | 1x| 9.8 | 1.040 | 7.1 | 41.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_1x_20181218-c9c69c8f.pth)
| X-101-64x4d-FPN | pytorch | 2x| | | | 40.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_2x_20181218-fe94f9b8.pth) | X-101-64x4d-FPN | pytorch | 2x| - | - | - | 40.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_x101_64x4d_fpn_2x_20181218-fe94f9b8.pth)
### Mask R-CNN ### Mask R-CNN
...@@ -76,14 +71,14 @@ More models with different backbones will be added to the model zoo. ...@@ -76,14 +71,14 @@ More models with different backbones will be added to the model zoo.
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:| |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN | caffe | 1x | 3.8 | 0.430 | 9.9 | 37.5 | 34.4 | - | | R-50-FPN | caffe | 1x | 3.8 | 0.430 | 9.9 | 37.5 | 34.4 | - |
| R-50-FPN | pytorch | 1x | 3.9 | 0.453 | 9.6 | 37.3 | 34.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth) | | R-50-FPN | pytorch | 1x | 3.9 | 0.453 | 9.6 | 37.3 | 34.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010-069fa190.pth) |
| R-50-FPN | pytorch | 2x | | | | 38.6 | 35.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_2x_20181010-41d35c05.pth) | | R-50-FPN | pytorch | 2x | - | - | - | 38.6 | 35.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_2x_20181010-41d35c05.pth) |
| R-101-FPN | caffe | 1x | 5.7 | 0.534 | 8.8 | 39.9 | 36.1 | - | | R-101-FPN | caffe | 1x | 5.7 | 0.534 | 8.8 | 39.9 | 36.1 | - |
| R-101-FPN | pytorch | 1x | 5.8 | 0.571 | 8.9 | 39.4 | 35.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_1x_20181129-34ad1961.pth) | | R-101-FPN | pytorch | 1x | 5.8 | 0.571 | 8.9 | 39.4 | 35.9 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_1x_20181129-34ad1961.pth) |
| R-101-FPN | pytorch | 2x | | | | 40.4 | 36.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_2x_20181129-a254bdfc.pth) | | R-101-FPN | pytorch | 2x | - | - | - | 40.4 | 36.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r101_fpn_2x_20181129-a254bdfc.pth) |
| X-101-32x4d-FPN | pytorch | 1x| 7.1 | 0.759 | 7.9 | 41.2 | 37.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_1x_20181218-44e635cc.pth) | X-101-32x4d-FPN | pytorch | 1x| 7.1 | 0.759 | 7.9 | 41.2 | 37.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_1x_20181218-44e635cc.pth)
| X-101-32x4d-FPN | pytorch | 2x| | | | 41.4 | 37.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_2x_20181218-f023dffa.pth) | X-101-32x4d-FPN | pytorch | 2x| - | - | - | 41.4 | 37.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_32x4d_fpn_2x_20181218-f023dffa.pth)
| X-101-64x4d-FPN | pytorch | 1x| 10.0 | 1.102 | 5.8 | 42.2 | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_1x_20181218-cb159987.pth) | X-101-64x4d-FPN | pytorch | 1x| 10.0 | 1.102 | 5.8 | 42.2 | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_1x_20181218-cb159987.pth)
| X-101-64x4d-FPN | pytorch | 2x| | | | 42.0 | 37.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_2x_20181218-ea936e44.pth) | X-101-64x4d-FPN | pytorch | 2x| - | - | - | 42.0 | 37.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_x101_64x4d_fpn_2x_20181218-ea936e44.pth)
### Fast R-CNN (with pre-computed proposals) ### Fast R-CNN (with pre-computed proposals)
...@@ -91,16 +86,16 @@ More models with different backbones will be added to the model zoo. ...@@ -91,16 +86,16 @@ More models with different backbones will be added to the model zoo.
|:--------:|:-------:|:------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:| |:--------:|:-------:|:------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN | caffe | Faster | 1x | 3.3 | 0.242 | 18.4 | 36.6 | - | - | | R-50-FPN | caffe | Faster | 1x | 3.3 | 0.242 | 18.4 | 36.6 | - | - |
| R-50-FPN | pytorch | Faster | 1x | 3.5 | 0.250 | 16.5 | 35.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_1x_20181010-08160859.pth) | | R-50-FPN | pytorch | Faster | 1x | 3.5 | 0.250 | 16.5 | 35.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_1x_20181010-08160859.pth) |
| R-50-FPN | pytorch | Faster | 2x | | | | 37.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_2x_20181010-d263ada5.pth) | | R-50-FPN | pytorch | Faster | 2x | - | - | - | 37.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_2x_20181010-d263ada5.pth) |
| R-101-FPN| caffe | Faster | 1x | 5.2 | 0.355 | 14.4 | 38.6 | - | - | | R-101-FPN| caffe | Faster | 1x | 5.2 | 0.355 | 14.4 | 38.6 | - | - |
| R-101-FPN| pytorch | Faster | 1x | 5.4 | 0.388 | 13.2 | 38.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_1x_20181129-ffaa2eb0.pth) | | R-101-FPN| pytorch | Faster | 1x | 5.4 | 0.388 | 13.2 | 38.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_1x_20181129-ffaa2eb0.pth) |
| R-101-FPN| pytorch | Faster | 2x | | | | 38.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_2x_20181129-9dba92ce.pth) | | R-101-FPN| pytorch | Faster | 2x | - | - | - | 38.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r101_fpn_2x_20181129-9dba92ce.pth) |
| R-50-FPN | caffe | Mask | 1x | 3.4 | 0.328 | 12.8 | 37.3 | 34.5 | - | | R-50-FPN | caffe | Mask | 1x | 3.4 | 0.328 | 12.8 | 37.3 | 34.5 | - |
| R-50-FPN | pytorch | Mask | 1x | 3.5 | 0.346 | 12.7 | 36.8 | 34.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_1x_20181010-e030a38f.pth) | | R-50-FPN | pytorch | Mask | 1x | 3.5 | 0.346 | 12.7 | 36.8 | 34.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_1x_20181010-e030a38f.pth) |
| R-50-FPN | pytorch | Mask | 2x | | | | 37.9 | 34.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth) | | R-50-FPN | pytorch | Mask | 2x | - | - | - | 37.9 | 34.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010-5048cb03.pth) |
| R-101-FPN| caffe | Mask | 1x | 5.2 | 0.429 | 11.2 | 39.4 | 36.1 | - | | R-101-FPN| caffe | Mask | 1x | 5.2 | 0.429 | 11.2 | 39.4 | 36.1 | - |
| R-101-FPN| pytorch | Mask | 1x | 5.4 | 0.462 | 10.9 | 38.9 | 35.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_1x_20181129-2273fa9b.pth) | | R-101-FPN| pytorch | Mask | 1x | 5.4 | 0.462 | 10.9 | 38.9 | 35.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_1x_20181129-2273fa9b.pth) |
| R-101-FPN| pytorch | Mask | 2x | | | | 39.9 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_2x_20181129-bf63ec5e.pth) | | R-101-FPN| pytorch | Mask | 2x | - | - | - | 39.9 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r101_fpn_2x_20181129-bf63ec5e.pth) |
### RetinaNet ### RetinaNet
...@@ -108,14 +103,14 @@ More models with different backbones will be added to the model zoo. ...@@ -108,14 +103,14 @@ More models with different backbones will be added to the model zoo.
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN | caffe | 1x | 3.4 | 0.285 | 12.5 | 35.8 | - | | R-50-FPN | caffe | 1x | 3.4 | 0.285 | 12.5 | 35.8 | - |
| R-50-FPN | pytorch | 1x | 3.6 | 0.308 | 12.1 | 35.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-3d3c2142.pth) | | R-50-FPN | pytorch | 1x | 3.6 | 0.308 | 12.1 | 35.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_1x_20181125-3d3c2142.pth) |
| R-50-FPN | pytorch | 2x | | | | 36.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_2x_20181125-e0dbec97.pth) | | R-50-FPN | pytorch | 2x | - | - | - | 36.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r50_fpn_2x_20181125-e0dbec97.pth) |
| R-101-FPN | caffe | 1x | 5.3 | 0.410 | 10.4 | 37.8 | - | | R-101-FPN | caffe | 1x | 5.3 | 0.410 | 10.4 | 37.8 | - |
| R-101-FPN | pytorch | 1x | 5.5 | 0.429 | 10.9 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_1x_20181129-f738a02f.pth) | | R-101-FPN | pytorch | 1x | 5.5 | 0.429 | 10.9 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_1x_20181129-f738a02f.pth) |
| R-101-FPN | pytorch | 2x | | | | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_2x_20181129-f654534b.pth) | | R-101-FPN | pytorch | 2x | - | - | - | 38.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_r101_fpn_2x_20181129-f654534b.pth) |
| X-101-32x4d-FPN | pytorch | 1x| 6.7 | 0.632 | 9.3 | 39.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_1x_20190501-967812ba.pth) | X-101-32x4d-FPN | pytorch | 1x| 6.7 | 0.632 | 9.3 | 39.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_1x_20190501-967812ba.pth)
| X-101-32x4d-FPN | pytorch | 2x| | | | 39.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_2x_20181218-605dcd0a.pth) | X-101-32x4d-FPN | pytorch | 2x| - | - | - | 39.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_32x4d_fpn_2x_20181218-605dcd0a.pth)
| X-101-64x4d-FPN | pytorch | 1x| 9.6 | 0.993 | 7.0 | 40.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_1x_20181218-2f6f778b.pth) | X-101-64x4d-FPN | pytorch | 1x| 9.6 | 0.993 | 7.0 | 40.0 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_1x_20181218-2f6f778b.pth)
| X-101-64x4d-FPN | pytorch | 2x| | | | 39.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_2x_20181218-2f598dc5.pth) | X-101-64x4d-FPN | pytorch | 2x| - | - | - | 39.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/retinanet_x101_64x4d_fpn_2x_20181218-2f598dc5.pth)
### Cascade R-CNN ### Cascade R-CNN
...@@ -123,14 +118,14 @@ More models with different backbones will be added to the model zoo. ...@@ -123,14 +118,14 @@ More models with different backbones will be added to the model zoo.
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:| |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN | caffe | 1x | 3.9 | 0.464 | 9.7 | 40.6 | - | | R-50-FPN | caffe | 1x | 3.9 | 0.464 | 9.7 | 40.6 | - |
| R-50-FPN | pytorch | 1x | 4.1 | 0.455 | 10.1 | 40.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_1x_20190501-3b6211ab.pth) | | R-50-FPN | pytorch | 1x | 4.1 | 0.455 | 10.1 | 40.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_1x_20190501-3b6211ab.pth) |
| R-50-FPN | pytorch | 20e | | | | 41.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_20e_20181123-db483a09.pth) | | R-50-FPN | pytorch | 20e | - | - | - | 41.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r50_fpn_20e_20181123-db483a09.pth) |
| R-101-FPN | caffe | 1x | 5.8 | 0.569 | 8.7 | 42.5 | - | | R-101-FPN | caffe | 1x | 5.8 | 0.569 | 8.7 | 42.5 | - |
| R-101-FPN | pytorch | 1x | 6.0 | 0.584 | 8.7 | 42.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_1x_20181129-d64ebac7.pth) | | R-101-FPN | pytorch | 1x | 6.0 | 0.584 | 8.7 | 42.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_1x_20181129-d64ebac7.pth) |
| R-101-FPN | pytorch | 20e | | | | 42.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_20e_20181129-b46dcede.pth) | | R-101-FPN | pytorch | 20e | - | - | - | 42.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_r101_fpn_20e_20181129-b46dcede.pth) |
| X-101-32x4d-FPN | pytorch | 1x| 7.2 | 0.770 | 7.8 | 43.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_1x_20190501-af628be5.pth) | X-101-32x4d-FPN | pytorch | 1x| 7.2 | 0.770 | 7.8 | 43.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_1x_20190501-af628be5.pth)
| X-101-32x4d-FPN | pytorch |20e| | | | 44.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_2x_20181218-28f73c4c.pth) | X-101-32x4d-FPN | pytorch |20e| - | - | - | 44.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_32x4d_fpn_2x_20181218-28f73c4c.pth)
| X-101-64x4d-FPN | pytorch | 1x| 10.0 | 1.133 | 6.1 | 44.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_1x_20181218-e2dc376a.pth) | X-101-64x4d-FPN | pytorch | 1x| 10.0 | 1.133 | 6.1 | 44.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_1x_20181218-e2dc376a.pth)
| X-101-64x4d-FPN | pytorch |20e| | | | 44.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_2x_20181218-5add321e.pth) | X-101-64x4d-FPN | pytorch |20e| - | - | - | 44.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_rcnn_x101_64x4d_fpn_2x_20181218-5add321e.pth)
### Cascade Mask R-CNN ### Cascade Mask R-CNN
...@@ -138,19 +133,18 @@ More models with different backbones will be added to the model zoo. ...@@ -138,19 +133,18 @@ More models with different backbones will be added to the model zoo.
|:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:| |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN | caffe | 1x | 5.1 | 0.692 | 6.7 | 41.0 | 35.6 | - | | R-50-FPN | caffe | 1x | 5.1 | 0.692 | 6.7 | 41.0 | 35.6 | - |
| R-50-FPN | pytorch | 1x | 5.3 | 0.683 | 6.5 | 41.3 | 35.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth) | | R-50-FPN | pytorch | 1x | 5.3 | 0.683 | 6.5 | 41.3 | 35.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_1x_20181123-88b170c9.pth) |
| R-50-FPN | pytorch | 20e | | | | 42.4 | 36.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_20e_20181123-6e0c9713.pth) | | R-50-FPN | pytorch | 20e | - | - | - | 42.4 | 36.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r50_fpn_20e_20181123-6e0c9713.pth) |
| R-101-FPN | caffe | 1x | 7.0 | 0.803 | 6.3 | 43.1 | 37.3 | - | | R-101-FPN | caffe | 1x | 7.0 | 0.803 | 6.3 | 43.1 | 37.3 | - |
| R-101-FPN | pytorch | 1x | 7.2 | 0.807 | 6.1 | 42.7 | 37.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_1x_20181129-64f00602.pth) | | R-101-FPN | pytorch | 1x | 7.2 | 0.807 | 6.1 | 42.7 | 37.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_1x_20181129-64f00602.pth) |
| R-101-FPN | pytorch | 20e | | | | 43.4 | 37.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_20e_20181129-cb85151d.pth) | | R-101-FPN | pytorch | 20e | - | - | - | 43.4 | 37.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_r101_fpn_20e_20181129-cb85151d.pth) |
| X-101-32x4d-FPN | pytorch | 1x| 8.4 | 0.976 | 5.7 | 44.4 | 38.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_1x_20181218-1d944c89.pth) | X-101-32x4d-FPN | pytorch | 1x| 8.4 | 0.976 | 5.7 | 44.4 | 38.3 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_1x_20181218-1d944c89.pth)
| X-101-32x4d-FPN | pytorch |20e| | | | 44.9 | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_20e_20181218-761a3473.pth) | X-101-32x4d-FPN | pytorch |20e| - | - | - | 44.9 | 38.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_32x4d_fpn_20e_20181218-761a3473.pth)
| X-101-64x4d-FPN | pytorch | 1x| 11.4 | 1.33 | 4.7 | 45.3 | 39.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_1x_20190501-827e0a70.pth) | X-101-64x4d-FPN | pytorch | 1x| 11.4 | 1.33 | 4.7 | 45.3 | 39.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_1x_20190501-827e0a70.pth)
| X-101-64x4d-FPN | pytorch |20e| | | | 45.8 | 39.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_20e_20181218-630773a7.pth) | X-101-64x4d-FPN | pytorch |20e| - | - | - | 45.8 | 39.5 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/cascade_mask_rcnn_x101_64x4d_fpn_20e_20181218-630773a7.pth)
**Notes:** **Notes:**
- The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs. - The `20e` schedule in Cascade (Mask) R-CNN indicates decreasing the lr at 16 and 19 epochs, with a total of 20 epochs.
- Cascade Mask R-CNN with X-101-64x4d-FPN was trained using 16 GPU with a batch size of 16 (1 images per GPU).
### Hybrid Task Cascade (HTC) ### Hybrid Task Cascade (HTC)
...@@ -190,7 +184,6 @@ Please refer to [HTC](configs/htc/README.md) for details. ...@@ -190,7 +184,6 @@ Please refer to [HTC](configs/htc/README.md) for details.
**Notes:** **Notes:**
- (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk). - (d) means pretrained model converted from Detectron, and (c) means the contributed model pretrained by [@thangvubk](https://github.com/thangvubk).
- The `3x` schedule is epoch [28, 34, 36]. - The `3x` schedule is epoch [28, 34, 36].
- The memory is measured with `torch.cuda.max_memory_allocated()` instead of `torch.cuda.max_memory_cached()`. We will update the memory usage of other models in the future.
### Deformable Convolution v2 ### Deformable Convolution v2
...@@ -213,14 +206,12 @@ Please refer to [HTC](configs/htc/README.md) for details. ...@@ -213,14 +206,12 @@ Please refer to [HTC](configs/htc/README.md) for details.
**Notes:** **Notes:**
- `dconv` and `mdconv` denote (modulated) deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` and `mdpool` denote (modulated) deformable roi pooling. - `dconv` and `mdconv` denote (modulated) deformable convolution, `c3-c5` means adding dconv in resnet stage 3 to 5. `dpool` and `mdpool` denote (modulated) deformable roi pooling.
- The memory is measured with `torch.cuda.max_memory_allocated()`. The batch size is 16 (2 images per GPU).
- The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster. - The dcn ops are modified from https://github.com/chengdazhi/Deformable-Convolution-V2-PyTorch, which should be more memory efficient and slightly faster.
## Comparison with Detectron ## Comparison with Detectron and maskrcnn-benchmark
We compare mmdetection with [Detectron](https://github.com/facebookresearch/Detectron) We compare mmdetection with [Detectron](https://github.com/facebookresearch/Detectron)
and [Detectron.pytorch](https://github.com/roytseng-tw/Detectron.pytorch), and [maskrcnn-benchmark](https://github.com/facebookresearch/maskrcnn-benchmark). The backbone used is R-50-FPN.
a third-party port of Detectron to Pytorch. The backbone used is R-50-FPN.
In general, mmdetection has 3 advantages over Detectron. In general, mmdetection has 3 advantages over Detectron.
...@@ -230,25 +221,22 @@ In general, mmdetection has 3 advantages over Detectron. ...@@ -230,25 +221,22 @@ In general, mmdetection has 3 advantages over Detectron.
### Performance ### Performance
Detectron and Detectron.pytorch use caffe-style ResNet as the backbone. Detectron and maskrcnn-benchmark use caffe-style ResNet as the backbone.
In order to utilize the PyTorch model zoo, we use pytorch-style ResNet in our experiments.
In the meanwhile, we train models with caffe-style ResNet in 1x experiments for comparison.
We find that pytorch-style ResNet usually converges slower than caffe-style ResNet,
thus leading to slightly lower results in 1x schedule, but the final results
of 2x schedule is higher.
We report results using both caffe-style (weights converted from We report results using both caffe-style (weights converted from
[here](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models)) [here](https://github.com/facebookresearch/Detectron/blob/master/MODEL_ZOO.md#imagenet-pretrained-models))
and pytorch-style (weights from the official model zoo) ResNet backbone, and pytorch-style (weights from the official model zoo) ResNet backbone,
indicated as *pytorch-style results* / *caffe-style results*. indicated as *pytorch-style results* / *caffe-style results*.
We find that pytorch-style ResNet usually converges slower than caffe-style ResNet,
thus leading to slightly lower results in 1x schedule, but the final results
of 2x schedule is higher.
<table> <table>
<tr> <tr>
<th>Type</th> <th>Type</th>
<th>Lr schd</th> <th>Lr schd</th>
<th>Detectron</th> <th>Detectron</th>
<th>Detectron.pytorch</th> <th>maskrcnn-benchmark</th>
<th>mmdetection</th> <th>mmdetection</th>
</tr> </tr>
<tr> <tr>
...@@ -268,7 +256,7 @@ indicated as *pytorch-style results* / *caffe-style results*. ...@@ -268,7 +256,7 @@ indicated as *pytorch-style results* / *caffe-style results*.
<td rowspan="2">Faster R-CNN</td> <td rowspan="2">Faster R-CNN</td>
<td>1x</td> <td>1x</td>
<td>36.7</td> <td>36.7</td>
<td>37.1</td> <td>36.8</td>
<td>36.4 / 36.7</td> <td>36.4 / 36.7</td>
</tr> </tr>
<tr> <tr>
...@@ -281,7 +269,7 @@ indicated as *pytorch-style results* / *caffe-style results*. ...@@ -281,7 +269,7 @@ indicated as *pytorch-style results* / *caffe-style results*.
<td rowspan="2">Mask R-CNN</td> <td rowspan="2">Mask R-CNN</td>
<td>1x</td> <td>1x</td>
<td>37.7 &amp; 33.9</td> <td>37.7 &amp; 33.9</td>
<td>37.7 &amp; 33.7</td> <td>37.8 &amp; 34.2</td>
<td>37.3 &amp; 34.2 / 37.5 &amp; 34.4</td> <td>37.3 &amp; 34.2 / 37.5 &amp; 34.4</td>
</tr> </tr>
<tr> <tr>
...@@ -326,51 +314,45 @@ The training speed is measure with s/iter. The lower, the better. ...@@ -326,51 +314,45 @@ The training speed is measure with s/iter. The lower, the better.
<tr> <tr>
<th>Type</th> <th>Type</th>
<th>Detectron (P100<sup>1</sup>)</th> <th>Detectron (P100<sup>1</sup>)</th>
<th>Detectron.pytorch (XP<sup>2</sup>)</th> <th>maskrcnn-benchmark (V100)</th>
<th>mmdetection<sup>3</sup> (V100<sup>4</sup> / XP)</th> <th>mmdetection (V100<sup>2</sup>)</th>
</tr> </tr>
<tr> <tr>
<td>RPN</td> <td>RPN</td>
<td>0.416</td> <td>0.416</td>
<td>-</td> <td>-</td>
<td>0.276 / 0.253</td> <td>0.253</td>
</tr> </tr>
<tr> <tr>
<td>Faster R-CNN</td> <td>Faster R-CNN</td>
<td>0.544</td> <td>0.544</td>
<td>1.015</td> <td>0.353</td>
<td>0.353 / 0.333</td> <td>0.333</td>
</tr> </tr>
<tr> <tr>
<td>Mask R-CNN</td> <td>Mask R-CNN</td>
<td>0.889</td> <td>0.889</td>
<td>1.435</td> <td>0.454</td>
<td>0.453 / 0.430</td> <td>0.430</td>
</tr> </tr>
<tr> <tr>
<td>Fast R-CNN</td> <td>Fast R-CNN</td>
<td>0.285</td> <td>0.285</td>
<td>-</td> <td>-</td>
<td>0.250 / 0.242</td> <td>0.242</td>
</tr> </tr>
<tr> <tr>
<td>Fast R-CNN (w/mask)</td> <td>Fast R-CNN (w/mask)</td>
<td>0.377</td> <td>0.377</td>
<td>-</td> <td>-</td>
<td>0.346 / 0.328</td> <td>0.328</td>
</tr> </tr>
</table> </table>
\*1. Detectron reports the speed on Facebook's Big Basin servers (P100), \*1. Facebook's Big Basin servers (P100/V100) is slightly faster than the servers we use. mmdetection can also run slightly faster on FB's servers.
on our V100 servers it is slower so we use the official reported values.
\*2. Detectron.pytorch does not report the runtime and we encountered some issue to
run it on V100, so we report the speed on TITAN XP.
\*3. The speed of pytorch-style ResNet is approximately 5% slower than caffe-style, \*2. For fair comparison, we list the caffe-style results here.
and we report the pytorch-style results here.
\*4. We also run the models on a DGX-1 server (P100) and the speed is almost the same as our V100 servers.
### Inference Speed ### Inference Speed
...@@ -380,50 +362,87 @@ The inference speed is measured with fps (img/s) on a single GPU. The higher, th ...@@ -380,50 +362,87 @@ The inference speed is measured with fps (img/s) on a single GPU. The higher, th
<tr> <tr>
<th>Type</th> <th>Type</th>
<th>Detectron (P100)</th> <th>Detectron (P100)</th>
<th>Detectron.pytorch (XP)</th> <th>maskrcnn-benchmark (V100)</th>
<th>mmdetection (V100 / XP)</th> <th>mmdetection (V100)</th>
</tr> </tr>
<tr> <tr>
<td>RPN</td> <td>RPN</td>
<td>12.5</td> <td>12.5</td>
<td>-</td> <td>-</td>
<td>17.7 / 16.9</td> <td>16.9</td>
</tr> </tr>
<tr> <tr>
<td>Faster R-CNN</td> <td>Faster R-CNN</td>
<td>10.3</td> <td>10.3</td>
<td></td> <td>7.9</td>
<td>12.5 / 12.9</td> <td>12.9</td>
</tr> </tr>
<tr> <tr>
<td>Mask R-CNN</td> <td>Mask R-CNN</td>
<td>8.5</td> <td>8.5</td>
<td></td> <td>7.7</td>
<td>9.6 / 9.9</td> <td>9.9</td>
</tr> </tr>
<tr> <tr>
<td>Fast R-CNN</td> <td>Fast R-CNN</td>
<td>12.5</td> <td>12.5</td>
<td></td> <td>-</td>
<td>16.5 / 18.4</td> <td>18.4</td>
</tr> </tr>
<tr> <tr>
<td>Fast R-CNN (w/mask)</td> <td>Fast R-CNN (w/mask)</td>
<td>9.9</td> <td>9.9</td>
<td></td> <td>-</td>
<td>12.7 / 12.8</td> <td>12.8</td>
</tr> </tr>
</table> </table>
### Training memory ### Training memory
We perform various tests and there is no doubt that mmdetection is more memory <table>
efficient than Detectron, and the main cause is the deep learning framework itself, not our efforts. <tr>
Besides, Caffe2 and PyTorch have different apis to obtain memory usage <th>Type</th>
whose implementation is not exactly the same. <th>Detectron</th>
<th>maskrcnn-benchmark</th>
<th>mmdetection</th>
</tr>
<tr>
<td>RPN</td>
<td>6.4</td>
<td>-</td>
<td>3.3</td>
</tr>
<tr>
<td>Faster R-CNN</td>
<td>7.2</td>
<td>4.4</td>
<td>3.6</td>
</tr>
<tr>
<td>Mask R-CNN</td>
<td>8.6</td>
<td>5.2</td>
<td>3.8</td>
</tr>
<tr>
<td>Fast R-CNN</td>
<td>6.0</td>
<td>-</td>
<td>3.3</td>
</tr>
<tr>
<td>Fast R-CNN (w/mask)</td>
<td>7.9</td>
<td>-</td>
<td>3.4</td>
</tr>
</table>
There is no doubt that maskrcnn-benchmark and mmdetection is more memory efficient than Detectron,
and the main advantage is PyTorch itself. We also perform some memory optimizations to push it forward.
Note that Caffe2 and PyTorch have different apis to obtain memory usage with different implementations.
For all codebases, `nvidia-smi` shows a larger memory usage than the reported number in the above table.
`nvidia-smi` shows a larger memory usage for both detectron and mmdetection, e.g.,
we observe a much higher memory usage when we train Mask R-CNN with 2 images per GPU using detectron (10.6G) and mmdetection (9.3G), which is obviously more than actually required.
> With mmdetection, we can train R-50 FPN Mask R-CNN with **4** images per GPU (TITAN XP, 12G),
which is a promising result.
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