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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
| 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) |
### 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 |
|:--------:|:-------:|:------:|:-------:|:--------:|:-------------------:|:--------------:|:------:|:-------:|:--------:|
| R-50-FPN | caffe | Faster | 1x | | | | | | |
| R-50-FPN | pytorch | Faster | 1x | | | | | | |
| R-50-FPN | pytorch | Faster | 2x | | | | | | |
| R-50-FPN | caffe | Mask | 1x | | | | | | |
| R-50-FPN | pytorch | Mask | 1x | | | | | | |
| R-50-FPN | pytorch | Mask | 2x | | | | | | |
| R-50-FPN | caffe | Faster | 1x | 3.5 | 0.35 | 14.6 | 36.6 | - | - |
| 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 | 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 | 5.4 | 0.47 | 10.7 | 37.3 | 34.5 | - |
| 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 | 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)
......@@ -95,8 +95,9 @@ In general, mmdetection has 3 advantages over Detectron.
### Performance
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,
thus leading to slightly lower results in 1x schedule, but the final results
of 2x schedule is higher.
......@@ -153,6 +154,32 @@ indicated as *pytorch-style results* / *caffe-style results*.
<td>-</td>
<td>38.6 &amp; 35.1 / -</td>
</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>
### Training Speed
......@@ -184,6 +211,18 @@ The training speed is measure with s/iter. The lower, the better.
<td>1.435</td>
<td>0.690 / 0.732</td>
</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>
\*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
<td></td>
<td>7.7 / 7.4</td>
</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>
### Training memory
......
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