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ModelZoo
SOLOv2-pytorch
Commits
e971324a
Commit
e971324a
authored
May 02, 2019
by
Cao Yuhang
Committed by
Kai Chen
May 02, 2019
Browse files
delete some wrong sentences (#572)
* delete some wrong sentences * fix speed in comparison section * minor fix
parent
7fb202b3
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e971324a
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@@ -15,8 +15,6 @@
-
CUDNN 7.0.4
-
NCCL 2.1.15
Note: The train time was measured with PyTorch 0.4.1. We will update it later, which should be about 0.02s ~ 0.05s faster.
## Mirror sites
We use AWS as the main site to host our model zoo, and maintain a mirror on aliyun.
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@@ -40,7 +38,7 @@ It should be very close to the actual memory and time.
## Baselines
We released RPN, Faster R-CNN and Mask R-CNN models in the first version.
More models with different backbones will be added to the model zoo.
More models with different backbones will be added to the model zoo.
### RPN
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@@ -335,31 +333,31 @@ The training speed is measure with s/iter. The lower, the better.
<td>
RPN
</td>
<td>
0.416
</td>
<td>
-
</td>
<td>
0.
407
/ 0.
41
3
</td>
<td>
0.
276
/ 0.
25
3
</td>
</tr>
<tr>
<td>
Faster R-CNN
</td>
<td>
0.544
</td>
<td>
1.015
</td>
<td>
0.
554
/ 0.
579
</td>
<td>
0.
353
/ 0.
333
</td>
</tr>
<tr>
<td>
Mask R-CNN
</td>
<td>
0.889
</td>
<td>
1.435
</td>
<td>
0.
690
/ 0.
732
</td>
<td>
0.
453
/ 0.
430
</td>
</tr>
<tr>
<td>
Fast R-CNN
</td>
<td>
0.285
</td>
<td>
-
</td>
<td>
0.
375
/ 0.
398
</td>
<td>
0.
250
/ 0.
242
</td>
</tr>
<tr>
<td>
Fast R-CNN (w/mask)
</td>
<td>
0.377
</td>
<td>
-
</td>
<td>
0.
504
/ 0.
574
</td>
<td>
0.
346
/ 0.
328
</td>
</tr>
</table>
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@@ -389,31 +387,31 @@ The inference speed is measured with fps (img/s) on a single GPU. The higher, th
<td>
RPN
</td>
<td>
12.5
</td>
<td>
-
</td>
<td>
1
4.5
/ 1
5.4
</td>
<td>
1
7.7
/ 1
6.9
</td>
</tr>
<tr>
<td>
Faster R-CNN
</td>
<td>
10.3
</td>
<td></td>
<td>
9.9 / 9.8
</td>
<td>
12.5 / 12.9
</td>
</tr>
<tr>
<td>
Mask R-CNN
</td>
<td>
8.5
</td>
<td></td>
<td>
7.7 / 7.4
</td>
<td>
9.6 / 9.9
</td>
</tr>
<tr>
<td>
Fast R-CNN
</td>
<td>
12.5
</td>
<td></td>
<td>
1
4
.5 / 1
4.1
</td>
<td>
1
6
.5 / 1
8.4
</td>
</tr>
<tr>
<td>
Fast R-CNN (w/mask)
</td>
<td>
9.9
</td>
<td></td>
<td>
1
0.6
/ 1
0.3
</td>
<td>
1
2.7
/ 1
2.8
</td>
</tr>
</table>
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