Python script to generate prototxt on Caffe, specially the inception_v3\inception_v4\inception_resnet\fractalnet
The prototxts can be visualized by ethereon.
Every model has a bn (batch normalization) version (maybe only bn version), the paper is Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
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.
- 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-v3 | 78.33/94.25 | 80.40/95.27 | 79.90/95.18 | 80.75/95.76 |
| 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.
- Top-1/5 accuracy with different crop sizes.
- Figure: Accuracy curves of inception_v3(left) and resnet101_v2(right) with different crop sizes.
- 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 |
-
Download the ILSVRC 2012 classification val set 6.3GB, and put the extracted images into the directory:
~/Database/ILSVRC2012 -
Check the resnet-v2 (101, 152 and 269) performance, the settings of evaluation_cls.py:
val_file = 'ILSVRC2012_val.txt' # download from this folder, label range 0~999 ... ... model_weights = 'resnet-v2/resnet101_v2.caffemodel' # download as below model_deploy = 'resnet-v2/deploy_resnet101_v2.prototxt' # check the parameters of input_shape ... ... mean_value = np.array([102.9801, 115.9465, 122.7717]) # BGR std = np.array([1.0, 1.0, 1.0]) # BGR crop_num = 1 # perform center(single)-cropCheck the inception-v3 performance, the settings of evaluation_cls.py:
val_file = 'ILSVRC2015_val.txt' # download from this folder, label range 0~999 ... ... model_weights = 'inception_v3/inception_v3.caffemodel' # download as below model_deploy = 'inception_v3/deploy_inception_v3.prototxt' # check the parameters of input_shape ... ... mean_value = np.array([128.0, 128.0, 128.0]) # BGR std = np.array([128.0, 128.0, 128.0]) # BGR crop_num = 1 # perform center(single)-cropCheck the inception-resnet-v2 (inception-v4) performance, the settings of evaluation_cls.py:
val_file = 'ILSVRC2012_val.txt' # download from this folder, label range 0~999 ... ... model_weights = 'inception_resnet_v2/inception_resnet_v2.caffemodel' # download as below model_deploy = 'inception_resnet_v2/deploy_inception_resnet_v2.prototxt' # check the parameters of input_shape ... ... mean_value = np.array([128.0, 128.0, 128.0]) # BGR std = np.array([128.0, 128.0, 128.0]) # BGR crop_num = 1 # perform center(single)-cropCheck the resnext (50_32x4d, 101_32x4d and 101_64x4d) or wrn50_2 performance, the settings of evaluation_cls.py:
val_file = 'ILSVRC2012_val.txt' # download from this folder, label range 0~999 ... ... model_weights = 'inception_resnet_v2/inception_resnet_v2.caffemodel' # download as below model_deploy = 'inception_resnet_v2/deploy_inception_resnet_v2.prototxt' # check the parameters of input_shape ... ... mean_value = np.array([103.52, 116.28, 123.675]) # BGR std = np.array([57.375, 57.12, 58.395]) # BGR crop_num = 1 # perform center(single)-crop -
then
python evaluation_cls.py
I greatly thank Yangqing Jia and BVLC group for developing Caffe
And I would like to thank all the authors of every cnn model