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)

https://github.com/soeaver/caffe-model/tree/master/cls

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

0. 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-resnet-v2	80.14/95.17	81.54/95.92	81.25/95.98	81.85/96.29

• All the pre-train models are tested on original caffe by evaluation_cls.py.
• 224x224(base_size=256) and 320x320(base_size=320) crop size for resnet-v2, 299x299(base_size=320) and 395x395(base_size=395) crop size for inception.

0. Top-1/5 accuracy with different crop sizes.

• Figure: Accuracy curves of inception_v3(left) and resnet101_v2(right) with different crop sizes.

0. Download url and forward time cost for each model.

Forward time cost is evaluated with one image/mini-batch using cuDNN 5.1 on a Pascal Titan X GPU.

Network	224/299	320/395	Download(BaiDuYun)
resnet101-v2	58.0ms	69.1ms	caffemodel (170.3MB)
resnet152-v2	84.6ms	100.8ms	caffemodel (230.2MB)
resnet269-v2	146.9ms	173.2ms	caffemodel (390.4MB)
inception-v3	58.3ms	82.8ms	caffemodel (91.1MB)
inception-resnet-v2	127.1ms	172.4ms	caffemodel (213.4MB)

Check the performance

0. Download the ILSVRC 2012 classification val set 6.3GB, and put the extracted images into the directory:

   ~/Database/ILSVRC2012
   

1. 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 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
   

2. 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