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Commit da19bd4f authored by Kai Chen's avatar Kai Chen
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update fast rcnn results

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MODEL_ZOO.md
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...@@ -60,16 +60,16 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m ...@@ -60,16 +60,16 @@ We released RPN, Faster R-CNN and Mask R-CNN models in the first version. More m
| 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 | 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 | 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)
| Backbone | Style | 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 | 3.5 | 0.35 | 14.6 | 36.6 | - | - |
| R-50-FPN | pytorch | Faster | 1x | | | | | | | | R-50-FPN | pytorch | Faster | 1x | 4.0 | 0.38 | 14.5 | 35.8 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_1x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_rcnn_r50_fpn_1x_20181010_results.pkl.json) |
| R-50-FPN | pytorch | Faster | 2x | | | | | | | | R-50-FPN | pytorch | Faster | 2x | 4.0 | 0.38 | 14.5 | 37.1 | - | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_2x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_rcnn_r50_fpn_2x_20181010_results.pkl.json) |
| R-50-FPN | caffe | Mask | 1x | | | | | | | | R-50-FPN | caffe | Mask | 1x | 5.4 | 0.47 | 10.7 | 37.3 | 34.5 | - |
| R-50-FPN | pytorch | Mask | 1x | | | | | | | | R-50-FPN | pytorch | Mask | 1x | 5.3 | 0.50 | 10.6 | 36.8 | 34.1 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_1x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_mask_rcnn_r50_fpn_1x_20181010_results.pkl.json) |
| R-50-FPN | pytorch | Mask | 2x | | | | | | | | R-50-FPN | pytorch | Mask | 2x | 5.3 | 0.50 | 10.6 | 37.9 | 34.8 | [model](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010.pth) \| [result](https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_mask_rcnn_r50_fpn_2x_20181010_results.pkl.json) |
### RetinaNet (coming soon) ### RetinaNet (coming soon)
...@@ -95,8 +95,9 @@ In general, mmdetection has 3 advantages over Detectron. ...@@ -95,8 +95,9 @@ In general, mmdetection has 3 advantages over Detectron.
### Performance ### Performance
Detectron and Detectron.pytorch use caffe-style ResNet as the backbone. Detectron and Detectron.pytorch use caffe-style ResNet as the backbone.
To simply utilize the PyTorch model zoo, we use pytorch-style ResNet in our experiments. 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, 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 thus leading to slightly lower results in 1x schedule, but the final results
of 2x schedule is higher. of 2x schedule is higher.
...@@ -153,6 +154,32 @@ indicated as *pytorch-style results* / *caffe-style results*. ...@@ -153,6 +154,32 @@ indicated as *pytorch-style results* / *caffe-style results*.
<td>-</td> <td>-</td>
<td>38.6 &amp; 35.1 / -</td> <td>38.6 &amp; 35.1 / -</td>
</tr> </tr>
<tr>
<td rowspan="2">Fast R-CNN</td>
<td>1x</td>
<td>36.4</td>
<td>-</td>
<td>35.8 / 36.6</td>
</tr>
<tr>
<td>2x</td>
<td>36.8</td>
<td>-</td>
<td>37.1 / -</td>
</tr>
<tr>
<td rowspan="2">Fast R-CNN (w/mask)</td>
<td>1x</td>
<td>37.3 &amp; 33.7</td>
<td>-</td>
<td>36.8 &amp; 34.1 / 37.3 &amp; 34.5</td>
</tr>
<tr>
<td>2x</td>
<td>37.7 &amp; 34.0</td>
<td>-</td>
<td>37.9 &amp; 34.8 / -</td>
</tr>
</table> </table>
### Training Speed ### Training Speed
...@@ -184,6 +211,18 @@ The training speed is measure with s/iter. The lower, the better. ...@@ -184,6 +211,18 @@ The training speed is measure with s/iter. The lower, the better.
<td>1.435</td> <td>1.435</td>
<td>0.690 / 0.732</td> <td>0.690 / 0.732</td>
</tr> </tr>
<tr>
<td>Fast R-CNN</td>
<td>0.285</td>
<td>-</td>
<td>0.375 / 0.398</td>
</tr>
<tr>
<td>Fast R-CNN (w/mask)</td>
<td>0.377</td>
<td>-</td>
<td>0.504 / 0.574</td>
</tr>
</table> </table>
\*1. Detectron reports the speed on Facebook's Big Basin servers (P100), \*1. Detectron reports the speed on Facebook's Big Basin servers (P100),
...@@ -226,6 +265,18 @@ The inference speed is measured with fps (img/s) on a single GPU. The higher, th ...@@ -226,6 +265,18 @@ The inference speed is measured with fps (img/s) on a single GPU. The higher, th
<td></td> <td></td>
<td>7.7 / 7.4</td> <td>7.7 / 7.4</td>
</tr> </tr>
<tr>
<td>Fast R-CNN</td>
<td>12.5</td>
<td></td>
<td>14.5 / 14.1</td>
</tr>
<tr>
<td>Fast R-CNN (w/mask)</td>
<td>9.9</td>
<td></td>
<td>10.6 / 10.3</td>
</tr>
</table> </table>
### Training memory ### Training memory
......
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