Awesome

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.

Clone the caffe-model repository

git clone https://github.com/soeaver/caffe-model --recursive

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-gallery、 tensorflow slim、 craftGBD、 ResNeXt、 DenseNet、 wide-residual-networks、 keras deep-learning-models、 ademxapp、 DPNs、Senet

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<br/>(single-crop)	224/299<br/>(12-crop)	320/395<br/>(single-crop)	320/395<br/>(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<br/>(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<br/> (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<br/> (batch=112)	11,497MB	3.2 img/s	8,409MB
densenet-201	77.53	3.9 img/s<br/> (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<br/>speed	training<br/>memory	testing<br/>speed	testing<br/>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.