Caffe-model
Python script to generate prototxt on Caffe, specially the inception_v3\inception_v4\inception_resnet\fractalnet
Scripts
The prototxts can be visualized by ethereon.
All the generator scripts have moved to scripts folder
CLS (imagenet)
Introduction
This folder contains the deploy files(include generator scripts) and pre-train models of resnet-v1, resnet-v2, inception-v3, inception-resnet-v2 and densenet(coming soon).
We didn't train any model from scratch, some of them are converted from other deep learning framworks (inception-v3 from mxnet, inception-resnet-v2 from tensorflow), some of them are converted from other modified caffe (resnet-v2). But to achieve the original performance, finetuning is performed on imagenet for several epochs.
The main contribution belongs to the authors and model trainers.
Performance on imagenet
1. Top-1/5 accuracy of pre-train models in this repository.
| Network | 224/299(single-crop) | 224/299(12-crop) | 320/395(single-crop) | 320/395(12-crop) |
|---|---|---|---|---|
| resnet101-v2 | 78.05/93.88 | 80.01/94.96 | 79.63/94.84 | 80.71/95.43 |
| resnet152-v2 | 79.15/94.58 | 80.76/95.32 | 80.34/95.26 | 81.16/95.68 |
| resnet269-v2 | 80.29/95.00 | 81.75/95.80 | 81.30/95.67 | 82.13/96.15 |
| inception-v2 | 71.57/90.29 | 73.39/91.45 | 72.83/91.34 | 74.14/92.16 |
| inception-v3 | 78.33/94.25 | 80.40/95.27 | 79.90/95.18 | 80.75/95.76 |
| xception | 79.10/94.51 | ../.. | 80.42/95.23 | ../.. |
| inception-v4 | 79.97/94.91 | 81.40/95.70 | 81.32/95.68 | 81.88/96.08 |
| inception-resnet-v2 | 80.14/95.17 | 81.54/95.92 | 81.25/95.98 | 81.85/96.29 |
| resnext50_32x4d | 77.63/93.69 | 79.47/94.65 | 78.90/94.47 | 79.63/94.97 |
| resnext101_32x4d | 78.70/94.21 | 80.53/95.11 | 80.09/95.03 | 80.81/95.41 |
| resnext101_64x4d | 79.40/94.59 | 81.12/95.41 | 80.74/95.37 | 81.52/95.69 |
| wrn50_2(resnet50_1x128d) | 77.87/93.87 | 79.91/94.94 | 79.32/94.72 | 80.17/95.13 |
- The pre-train models are tested on original caffe by evaluation_cls.py, but ceil_mode:false（pooling_layer） is used for the models converted from torch, the detail in https://github.com/BVLC/caffe/pull/3057/files. If you remove ceil_mode:false, the performance will decline about 1% top1.
- 224x224(base_size=256) and 320x320(base_size=320) crop size for resnet-v2/resnext/wrn, 299x299(base_size=320) and 395x395(base_size=395) crop size for inception. Specially, 231x231 and 327x327 crop size for inception-v2.
2. Top-1/5 accuracy with different crop sizes.
- Figure: Accuracy curves of inception_v3(left) and resnet101_v2(right) with different crop sizes.
3. Download url and forward/backward time cost for each model.
Forward/Backward time cost is evaluated with one image/mini-batch using cuDNN 5.1 on a Pascal Titan X GPU.
We use
~/caffe/build/tools/caffe -model deploy.prototxt time -gpu -iterations 1000
to test the forward/backward time cost, the result is really different with time cost of evaluation_cls.py
| Network | F/B(224/299) | F/B(320/395) | Download | Source |
|---|---|---|---|---|
| resnet101-v2 | 22.31/22.75ms | 26.02/29.50ms | 170.3MB | craftGBD |
| resnet152-v2 | 32.11/32.54ms | 37.46/41.84ms | 230.2MB | craftGBD |
| resnet269-v2 | 58.20/59.15ms | 69.43/77.26ms | 390.4MB | craftGBD |
| inception-v3 | 21.79/19.82ms | 22.14/24.88ms | 91.1MB | mxnet |
| inception-v4 | 32.96/32.19ms | 36.04/41.91ms | 163.1MB | tensorflow_slim |
| inception-resnet-v2 | 49.06/54.83ms | 54.06/66.38ms | 213.4MB | tensorflow_slim |
| resnext50_32x4d | 17.29/20.08ms | 19.02/23.81ms | 95.8MB | facebookresearch |
| resnext101_32x4d | 30.73/35.75ms | 34.33/41.02ms | 169.1MB | facebookresearch |
| resnext101_64x4d | 42.07/64.58ms | 51.99/77.71ms | 319.2MB | facebookresearch |
| wrn50_2(resnet50_1x128d) | 16.48/25.28ms | 20.99/35.04ms | 263.1MB | szagoruyko |
Check the performance
1. Download the ILSVRC 2012 classification val set 6.3GB, and put the extracted images into the directory:
~/Database/ILSVRC2012
2. Modify the parameter settings
| Network | val_file | mean_value | std |
|---|---|---|---|
| resnet_v2(101/152/269) | ILSVRC2012_val | [102.98, 115.947, 122.772] | [1.0, 1.0, 1.0] |
| resnet_v2(38a/38a1) | ILSVRC2012_val | [103.52, 116.28, 123.675] | [57.375, 57.12, 58.395] |
| resnext(50/101), wrn50_2 | ILSVRC2012_val | [103.52, 116.28, 123.675] | [57.375, 57.12, 58.395] |
| inception-v3 | ILSVRC2015_val | [128.0, 128.0, 128.0] | [128.0, 128.0, 128.0] |
| inception-v2(xception) | ILSVRC2012_val | [128.0, 128.0, 128.0] | [128.0, 128.0, 128.0] |
| inception-v4(inception-resnet-v2) | ILSVRC2012_val | [128.0, 128.0, 128.0] | [128.0, 128.0, 128.0] |
3. then run evaluation_cls.py
python evaluation_cls.py
Acknowlegement
I greatly thank Yangqing Jia and BVLC group for developing Caffe
And I would like to thank all the authors of every cnn model