Skip to content

Caffe models (includimg classification, detection and segmentation) and deploy files for famouse networks

License

Notifications You must be signed in to change notification settings

danielanojan/caffe-model

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Caffe-model

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.

We recommend using these caffe models with py-RFCN-priv

Please install py-RFCN-priv for evaluating and finetuning.

Disclaimer

Most of the pre-train models are converted from other projects, the main contribution belongs to the original authors.

Project links:

mxnet-model-gallerytensorflow slimcraftGBDResNeXtDenseNetwide-residual-networkskeras deep-learning-modelsademxappDPNsSenet

CLS (Classification, more details are in cls)

Performance on imagenet validation.

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 ../..

DET (Detection, more details are in det)

Object Detection Performance on PASCAL VOC.

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

SEG (Segmentation, more details are in seg)

Object Segmentation Performance on PASCAL VOC.

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

License

caffe-model is released under the MIT License (refer to the LICENSE file for details).

Acknowlegement

I greatly thank Yangqing Jia and BVLC group for developing Caffe.

And I would like to thank all the authors of every network.

About

Caffe models (includimg classification, detection and segmentation) and deploy files for famouse networks

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 100.0%