Caffe models (including classification, detection and segmentation) and deploy files for famouse networks
Python
1,278
874 commits
updated Mar 22, 2018
Caffe models (include classification, detection and segmentation) and deploy prototxt for resnet, resnext, inception_v3, inception_v4, inception_resnet, wider_resnet, densenet, aligned-inception-resne(x)t, DPNs and other networks.
Clone the caffe-model repository
git clone https://github.com/soeaver/caffe-model --recursive
Please install py-RFCN-priv for evaluating and finetuning.
Most of the pre-train models are converted from other projects, the main contribution belongs to the original authors.
Project links:
mxnet-model-gallery、 tensorflow slim、 craftGBD、 ResNeXt、 DenseNet、 wide-residual-networks、 keras deep-learning-models、 ademxapp、 DPNs、Senet
Top-1/5 error of pre-train models in this repository (Pre-train models download urls).
| Network | 224/299 (single-crop) | 224/299 (12-crop) | 320/395 (single-crop) | 320/395 (12-crop) |
|---|---|---|---|---|
| resnet101-v2 | 21.95/6.12 | 19.99/5.04 | 20.37/5.16 | 19.29/4.57 |
| resnet152-v2 | 20.85/5.42 | 19.24/4.68 | 19.66/4.73 | 18.84/4.32 |
| resnet269-v2 | 19.71/5.00 | 18.25/4.20 | 18.70/4.33 | 17.87/3.85 |
| inception-v3 | 21.67/5.75 | 19.60/4.73 | 20.10/4.82 | 19.25/4.24 |
| xception | 20.90/5.49 | 19.68/4.90 | 19.58/4.77 | 18.91/4.39 |
| inception-v4 | 20.03/5.09 | 18.60/4.30 | 18.68/4.32 | 18.12/3.92 |
| inception-resnet-v2 | 19.86/4.83 | 18.46/4.08 | 18.75/4.02 | 18.15/3.71 |
| resnext50-32x4d | 22.37/6.31 | 20.53/5.35 | 21.10/5.53 | 20.37/5.03 |
| resnext101-32x4d | 21.30/5.79 | 19.47/4.89 | 19.91/4.97 | 19.19/4.59 |
| resnext101-64x4d | 20.60/5.41 | 18.88/4.59 | 19.26/4.63 | 18.48/4.31 |
| wrn50-2 (resnet50-1x128d) | 22.13/6.13 | 20.09/5.06 | 20.68/5.28 | 19.83/4.87 |
| air101 | 21.32/5.76 | 19.36/4.84 | 19.92/4.75 | 19.05/4.43 |
| dpn-92 | 20.81/5.47 | 18.99/4.59 | 19.23/4.64 | 18.68/4.24 |
| dpn-107 | 19.70/5.06 | ../.. | 18.41/4.25 | ../.. |
Original faster rcnn train on VOC 2007+2012 trainval and test on VOC 2007 test.
| Network | mAP@50 | train speed | train memory | test speed | test memory |
|---|---|---|---|---|---|
| resnet18 | 70.02 | 9.5 img/s | 1,235MB | 17.5 img/s | 989MB |
| resnet101-v2 | 79.6 | 3.1 img/s | 6,495MB | 7.1 img/s | 4,573MB |
| resnet152-v2 | 80.72 | 2.8 img/s | 9,315MB | 6.2 img/s | 6,021MB |
| wrn50-2 | 78.59 | 2.1 img/s | 4,895MB | 4.9 img/s | 3,499MB |
| resnext50-32x4d | 77.99 | 3.6 img/s | 5,315MB | 7.4 img/s | 4,305MB |
| resnext101-32x4d | 79.98 | 2.7 img/s | 7,836MB | 6.3 img/s | 5,705MB |
| resnext101-64x4d | 80.71 | 2.0 img/s (batch=96) | 11,277MB | 3.7 img/s | 9,461MB |
| inception-v3 | 78.6 | 4.1 img/s | 4,325MB | 7.3 img/s | 3,445MB |
| inception-v4 | 81.49 | 2.6 img/s | 6,759MB | 5.4 img/s | 4,683MB |
| inception-resnet-v2 | 80.0 | 2.0 img/s (batch=112) | 11,497MB | 3.2 img/s | 8,409MB |
| densenet-201 | 77.53 | 3.9 img/s (batch=72) | 10,073MB | 5.5 img/s | 9,955MB |
| resnet38a | 80.1 | 1.4 img/s | 8,723MB | 3.4 img/s | 5,501MB |
PSPNet training on SBD (10,582 images) and testing on VOC 2012 validation (1,449 images).
| Network | mIoU(%) | pixel acc(%) | training speed | training memory | testing speed | testing memory |
|---|---|---|---|---|---|---|
| resnet101-v2 | 77.94 | 94.94 | 1.6 img/s | 8,023MB | 3.0 img/s | 4,071MB |
| resnet101-v2-selu | 77.10 | 94.80 | 1.6 img/s | 8,017MB | 3.0 img/s | 4,065MB |
| resnext101-32x4d | 77.79 | 94.92 | 1.3 img/s | 8,891MB | 2.6 img/s | 5,241MB |
| air101 | 77.64 | 94.93 | 1.3 img/s | 10,017MB | 2.5 img/s | 5,241MB |
| inception-v4 | 77.58 | 94.83 | -- img/s | --MB | -- img/s | --MB |
caffe-model is released under the MIT License (refer to the LICENSE file for details).
I greatly thank Yangqing Jia and BVLC group for developing Caffe.
And I would like to thank all the authors of every network.
153 followers · starred Dec 2017
129 followers · starred May 2017
234 followers · starred Nov 2017
1,332 followers · starred Nov 2016
Caffe models (including classification, detection and segmentation) and deploy files for famouse networks
Python
1,278
874 commits
updated Mar 22, 2018
Caffe models (include classification, detection and segmentation) and deploy prototxt for resnet, resnext, inception_v3, inception_v4, inception_resnet, wider_resnet, densenet, aligned-inception-resne(x)t, DPNs and other networks.
Clone the caffe-model repository
git clone https://github.com/soeaver/caffe-model --recursive
Please install py-RFCN-priv for evaluating and finetuning.
Most of the pre-train models are converted from other projects, the main contribution belongs to the original authors.
Project links:
mxnet-model-gallery、 tensorflow slim、 craftGBD、 ResNeXt、 DenseNet、 wide-residual-networks、 keras deep-learning-models、 ademxapp、 DPNs、Senet
Top-1/5 error of pre-train models in this repository (Pre-train models download urls).
| Network | 224/299 (single-crop) | 224/299 (12-crop) | 320/395 (single-crop) | 320/395 (12-crop) |
|---|---|---|---|---|
| resnet101-v2 | 21.95/6.12 | 19.99/5.04 | 20.37/5.16 | 19.29/4.57 |
| resnet152-v2 | 20.85/5.42 | 19.24/4.68 | 19.66/4.73 | 18.84/4.32 |
| resnet269-v2 | 19.71/5.00 | 18.25/4.20 | 18.70/4.33 | 17.87/3.85 |
| inception-v3 | 21.67/5.75 | 19.60/4.73 | 20.10/4.82 | 19.25/4.24 |
| xception | 20.90/5.49 | 19.68/4.90 | 19.58/4.77 | 18.91/4.39 |
| inception-v4 | 20.03/5.09 | 18.60/4.30 | 18.68/4.32 | 18.12/3.92 |
| inception-resnet-v2 | 19.86/4.83 | 18.46/4.08 | 18.75/4.02 | 18.15/3.71 |
| resnext50-32x4d | 22.37/6.31 | 20.53/5.35 | 21.10/5.53 | 20.37/5.03 |
| resnext101-32x4d | 21.30/5.79 | 19.47/4.89 | 19.91/4.97 | 19.19/4.59 |
| resnext101-64x4d | 20.60/5.41 | 18.88/4.59 | 19.26/4.63 | 18.48/4.31 |
| wrn50-2 (resnet50-1x128d) | 22.13/6.13 | 20.09/5.06 | 20.68/5.28 | 19.83/4.87 |
| air101 | 21.32/5.76 | 19.36/4.84 | 19.92/4.75 | 19.05/4.43 |
| dpn-92 | 20.81/5.47 | 18.99/4.59 | 19.23/4.64 | 18.68/4.24 |
| dpn-107 | 19.70/5.06 | ../.. | 18.41/4.25 | ../.. |
Original faster rcnn train on VOC 2007+2012 trainval and test on VOC 2007 test.
| Network | mAP@50 | train speed | train memory | test speed | test memory |
|---|---|---|---|---|---|
| resnet18 | 70.02 | 9.5 img/s | 1,235MB | 17.5 img/s | 989MB |
| resnet101-v2 | 79.6 | 3.1 img/s | 6,495MB | 7.1 img/s | 4,573MB |
| resnet152-v2 | 80.72 | 2.8 img/s | 9,315MB | 6.2 img/s | 6,021MB |
| wrn50-2 | 78.59 | 2.1 img/s | 4,895MB | 4.9 img/s | 3,499MB |
| resnext50-32x4d | 77.99 | 3.6 img/s | 5,315MB | 7.4 img/s | 4,305MB |
| resnext101-32x4d | 79.98 | 2.7 img/s | 7,836MB | 6.3 img/s | 5,705MB |
| resnext101-64x4d | 80.71 | 2.0 img/s (batch=96) | 11,277MB | 3.7 img/s | 9,461MB |
| inception-v3 | 78.6 | 4.1 img/s | 4,325MB | 7.3 img/s | 3,445MB |
| inception-v4 | 81.49 | 2.6 img/s | 6,759MB | 5.4 img/s | 4,683MB |
| inception-resnet-v2 | 80.0 | 2.0 img/s (batch=112) | 11,497MB | 3.2 img/s | 8,409MB |
| densenet-201 | 77.53 | 3.9 img/s (batch=72) | 10,073MB | 5.5 img/s | 9,955MB |
| resnet38a | 80.1 | 1.4 img/s | 8,723MB | 3.4 img/s | 5,501MB |
PSPNet training on SBD (10,582 images) and testing on VOC 2012 validation (1,449 images).
| Network | mIoU(%) | pixel acc(%) | training speed | training memory | testing speed | testing memory |
|---|---|---|---|---|---|---|
| resnet101-v2 | 77.94 | 94.94 | 1.6 img/s | 8,023MB | 3.0 img/s | 4,071MB |
| resnet101-v2-selu | 77.10 | 94.80 | 1.6 img/s | 8,017MB | 3.0 img/s | 4,065MB |
| resnext101-32x4d | 77.79 | 94.92 | 1.3 img/s | 8,891MB | 2.6 img/s | 5,241MB |
| air101 | 77.64 | 94.93 | 1.3 img/s | 10,017MB | 2.5 img/s | 5,241MB |
| inception-v4 | 77.58 | 94.83 | -- img/s | --MB | -- img/s | --MB |
caffe-model is released under the MIT License (refer to the LICENSE file for details).
I greatly thank Yangqing Jia and BVLC group for developing Caffe.
And I would like to thank all the authors of every network.
153 followers · starred Dec 2017
129 followers · starred May 2017
234 followers · starred Nov 2017
1,332 followers · starred Nov 2016