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Joel Joseph
food-round2
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da19bd4f
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da19bd4f
authored
6 years ago
by
Kai Chen
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update fast rcnn results
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MODEL_ZOO.md
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da19bd4f
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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
)
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| 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 |
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| R-50-FPN | pytorch | Faster | 1x |
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| R-50-FPN | pytorch | Faster | 2x |
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| R-50-FPN | caffe | Mask | 1x |
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| R-50-FPN | pytorch | Mask | 1x |
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| R-50-FPN | pytorch | Mask | 2x |
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| R-50-FPN | caffe | Faster | 1x |
3.5
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0.35
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14.6
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36.6
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| R-50-FPN | pytorch | Faster | 1x |
4.0
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[
model
](
https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_1x_20181010.pth
)
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[
result
](
https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_rcnn_r50_fpn_1x_20181010_results.pkl.json
)
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| R-50-FPN | pytorch | Faster | 2x |
4.0
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0.38
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14.5
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[
model
](
https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_rcnn_r50_fpn_2x_20181010.pth
)
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[
result
](
https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_rcnn_r50_fpn_2x_20181010_results.pkl.json
)
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| R-50-FPN | caffe | Mask | 1x |
5.4
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0.47
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10.7
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37.3
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34.5
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| R-50-FPN | pytorch | Mask | 1x |
5.3
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34.1
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[
model
](
https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_1x_20181010.pth
)
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[
result
](
https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_mask_rcnn_r50_fpn_1x_20181010_results.pkl.json
)
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| R-50-FPN | pytorch | Mask | 2x |
5.3
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0.50
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34.8
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[
model
](
https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/models/fast_mask_rcnn_r50_fpn_2x_20181010.pth
)
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[
result
](
https://s3.ap-northeast-2.amazonaws.com/open-mmlab/mmdetection/results/fast_mask_rcnn_r50_fpn_2x_20181010_results.pkl.json
)
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### RetinaNet (coming soon)
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@@ -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.
...
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@@ -153,6 +154,32 @@ indicated as *pytorch-style results* / *caffe-style results*.
<td>
-
</td>
<td>
38.6
&
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
&
33.7
</td>
<td>
-
</td>
<td>
36.8
&
34.1 / 37.3
&
34.5
</td>
</tr>
<tr>
<td>
2x
</td>
<td>
37.7
&
34.0
</td>
<td>
-
</td>
<td>
37.9
&
34.8 / -
</td>
</tr>
</table>
### Training Speed
...
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@@ -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),
...
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@@ -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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