This project aims to help people who know some meachine learning algorithms or deep learning's,but have no idea how to begin a task using what they have learnt. And in this repository,I have added some CNNs to our models.What you need to do is to change the model name in config.py.

Networks in our preoject:

• LeNet
• AlexNet
• VGGNet
• ZFNet
• GoogLeNet
• ResNet_18/34/50/101/152
• DenseNet_161

#0.Requirements keras >=2.1.5 tensorflow >=1.8 opencv-python >= 3.4.0.12

1.Project structure

├─data             
│  ├─test          
│  └─train         
│      ├─00000     
│      ├─00001     
│      ├─00002     
│      ├─00003     
│      ├─00004     
│      ├─00005     
│      ├─00006     
│      ├─00007     
│      ├─00008     
│      └─00009     
├─log              
└─src              
    │  config.py
    │  train.py
    │  utils.py
    │  predict.py
    │
    ├─models
    │  │  AlexNet.py
    │  │  DenseNet.py
    │  │  GooLeNet.py
    │  │  LeNet.py
    │  │  resnet.py
    │  │  VGGNet.py
    │  │  ZFNet.py 

2.how to use

2.1 for data

You need to add your deferent category images to the folder "train/",and make a new floder to store your images.For example you have some dog's images ,you can makedir "data/train/dog/",and move your images to it.

2.2 for models

If you want to change the model ,the only thing you need to do is to change the parameter "model_name" in "config.py". Then do :

python train.py

2.3 for using the trained model

run:

python predict.py

3. references

Lécun Y, Bottou L, Bengio Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11):2278-2324.
Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012:1097-1105.
Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition[J]. Computer Science, 2014.
Zeiler M D, Fergus R. Visualizing and Understanding Convolutional Networks[J]. 2014, 8689:818-833.
He K, Zhang X, Ren S, et al. Deep Residual Learning for Image Recognition[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society, 2016:770-778.
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions[C]// IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2015:1-9.
Huang G, Liu Z, Laurens V D M, et al. Densely Connected Convolutional Networks[J]. 2016:2261-2269.
Introduce the cnns from LeNet to DensNet
DenseNet-Keras
keras-resnet