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Commit 4453afe4 authored by Kai Chen's avatar Kai Chen
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MODEL_ZOO.md
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...@@ -38,46 +38,46 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m ...@@ -38,46 +38,46 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m
### RPN ### RPN
| Backbone | Type | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR1000 | Download | | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | AR1000 | Download |
| ------------------ | ---- | ------- | -------- | ---------- | -------- | ------ | -------- | |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN (caffe) | RPN | 1x | 4.5 | 0.379 | 14.4 | 58.2 | - | | R-50-FPN | caffe | 1x | 4.5 | 0.379 | 14.4 | 58.2 | - |
| R-50-FPN (pytorch) | RPN | 1x | 4.8 | 0.407 | 14.5 | 57.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_1x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/rpn_r50_fpn_1x_20181010_results.pkl.json) | | R-50-FPN | pytorch | 1x | 4.8 | 0.407 | 14.5 | 57.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_1x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/rpn_r50_fpn_1x_20181010_results.pkl.json) |
| R-50-FPN (pytorch) | RPN | 2x | 4.8 | 0.407 | 14.5 | 57.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_2x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/rpn_r50_fpn_2x_20181010_results.pkl.json) | | R-50-FPN | pytorch | 2x | 4.8 | 0.407 | 14.5 | 57.6 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/rpn_r50_fpn_2x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/rpn_r50_fpn_2x_20181010_results.pkl.json) |
### Faster R-CNN ### Faster R-CNN
| Backbone | Type | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download | | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
| ------------------ | ------ | ------- | -------- | ---------- | -------- | ------ | -------- | |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN (caffe) | Faster | 1x | 4.9 | 0.525 | 10.0 | 36.7 | - | | R-50-FPN | caffe | 1x | 4.9 | 0.525 | 10.0 | 36.7 | - |
| R-50-FPN (pytorch) | Faster | 1x | 5.1 | 0.554 | 9.9 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/faster_rcnn_r50_fpn_1x_20181010_results.pkl.json) | | R-50-FPN | pytorch | 1x | 5.1 | 0.554 | 9.9 | 36.4 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_1x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/faster_rcnn_r50_fpn_1x_20181010_results.pkl.json) |
| R-50-FPN (pytorch) | Faster | 2x | 5.1 | 0.554 | 9.9 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_2x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/faster_rcnn_r50_fpn_2x_20181010_results.pkl.json) | | R-50-FPN | pytorch | 2x | 5.1 | 0.554 | 9.9 | 37.7 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/faster_rcnn_r50_fpn_2x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/faster_rcnn_r50_fpn_2x_20181010_results.pkl.json) |
### Mask R-CNN ### Mask R-CNN
| Backbone | Type | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | | Backbone | Style | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download |
| ------------------ | ---- | ------- | -------- | ---------- | -------- | ------ | ------- | -------- | |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN (caffe) | Mask | 1x | 5.9 | 0.658 | 7.7 | 37.5 | 34.4 | - | | R-50-FPN | caffe | 1x | 5.9 | 0.658 | 7.7 | 37.5 | 34.4 | - |
| R-50-FPN (pytorch) | Mask | 1x | 5.8 | 0.690 | 7.7 | 37.3 | 34.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/mask_rcnn_r50_fpn_1x_20181010_results.pkl.json) | | R-50-FPN | pytorch | 1x | 5.8 | 0.690 | 7.7 | 37.3 | 34.2 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_1x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/mask_rcnn_r50_fpn_1x_20181010_results.pkl.json) |
| R-50-FPN (pytorch) | Mask | 2x | 5.8 | 0.690 | 7.7 | 38.6 | 35.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_2x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/mask_rcnn_r50_fpn_2x_20181010_results.pkl.json) | | R-50-FPN | pytorch | 2x | 5.8 | 0.690 | 7.7 | 38.6 | 35.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/mask_rcnn_r50_fpn_2x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/mask_rcnn_r50_fpn_2x_20181010_results.pkl.json) |
### Fast R-CNN (with pre-computed proposals) (coming soon) ### Fast R-CNN (with pre-computed proposals) (coming soon)
| Backbone | Type | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | | Backbone | Style | Type | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download |
| ------------------ | ------ | ------- | -------- | ---------- | -------- | ------ | ------ | -------- | |:--------:|:-------:|:------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN (caffe) | Faster | 1x | | | | | | | | R-50-FPN | caffe | Faster | 1x | | | | | | |
| R-50-FPN (pytorch) | Faster | 1x | | | | | | | | R-50-FPN | pytorch | Faster | 1x | | | | | | |
| R-50-FPN (pytorch) | Faster | 2x | | | | | | | | R-50-FPN | pytorch | Faster | 2x | | | | | | |
| R-50-FPN (caffe) | Mask | 1x | | | | | | | | R-50-FPN | caffe | Mask | 1x | | | | | | |
| R-50-FPN (pytorch) | Mask | 1x | | | | | | | | R-50-FPN | pytorch | Mask | 1x | | | | | | |
| R-50-FPN (pytorch) | Mask | 2x | | | | | | | | R-50-FPN | pytorch | Mask | 2x | | | | | | |
### RetinaNet (coming soon) ### RetinaNet (coming soon)
| Backbone | Type | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | mask AP | Download | | Backbone | Lr schd | Mem (GB) | Train time (s/iter) | Inf time (fps) | box AP | Download |
| ------------------ | --------- | ------- | --------- | ---------- | -------- | ------ | ------- | -------- | |:--------:|:-------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:--------:|
| R-50-FPN (caffe) | RetinaNet | 1x | | | | | | | | R-50-FPN | caffe | 1x | | | | | |
| R-50-FPN (pytorch) | RetinaNet | 1x | | | | | | | | R-50-FPN | pytorch | 1x | | | | | |
| R-50-FPN (pytorch) | RetinaNet | 2x | | | | | | | | R-50-FPN | pytorch | 2x | | | | | |
## Comparison with Detectron ## Comparison with Detectron
...@@ -86,6 +86,12 @@ We compare mmdetection with [Detectron](https://github.com/facebookresearch/Dete ...@@ -86,6 +86,12 @@ We compare mmdetection with [Detectron](https://github.com/facebookresearch/Dete
and [Detectron.pytorch](https://github.com/roytseng-tw/Detectron.pytorch), and [Detectron.pytorch](https://github.com/roytseng-tw/Detectron.pytorch),
a third-party port of Detectron to Pytorch. 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.
- **Higher performance** (especially in terms of mask AP)
- **Faster training speed**
- **Memory efficient**
### Performance ### Performance
Detectron and Detectron.pytorch use caffe-style ResNet as the backbone. Detectron and Detectron.pytorch use caffe-style ResNet as the backbone.
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