Caffe-model

Python script to generate prototxt on Caffe, specially the inception_v3\inception_v4\inception_resnet\fractalnet

Generator scripts

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

Classificaiton (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

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

Check the performance

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)-crop

Check 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)-crop

Check 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)-crop

Check 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
```

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

liangzimei / caffe-model