ResNet-Pytorch-Face-Recognition

Using Pytorch to implement a ResNet50 for Cross-Age Face Recognition
Generally speaking, Pytorch is much more user-friendly than Tensorflow for academic purpose.

Prepare Dataset and Environment

Trainable ResNet50 using Python3.5 + Pytorch
DataSet: Cross-Age Celebrity Dataset(CACD)
By default, you should put all the CACD images under "./CACD2000/". If your dataset is in another place, add "--root-path your_path" when you run main.py.
You don't need to crop image into 2242243 size anymore, pytorchvision provide convenient "transforms" to do so.

Explanation of Each File

1. main.py is just used to control parameters, it doesn't contains any useful details.
2. ResNet.py & VGG.py are the cores of the project, they have the implementation of Networks.
3. train.py contains the details of training process.
4. data.py maintains a Class to generate CACD data class, which is very different with Tensorflow and quite useful.
5. In /model/params.pkl, we give a pretrained model learnt through default setting (change number of epoch to 30)

Training Part

1. Run main.py directly, there are some options and parameters you can modify, pleace check the details of main.py.
2. About "--model", there are 3 options: resnet50, resnet101, vgg16. Although I also implemented VGG class, but I didn't check whether it's working or not, so the default option is resnet50.
3. Labels and Image names are loaded from "./data/label.npy" and "./data/name.npy".
   Pytorch provide a very useful approach to create datasets. Please check data.py
4. Label is range from [1, LABELSNUM], to make correct classification, we should change it to [0, LABELNUM-1]
5. If you want to load a existing model, you should run the following "python main.py --model-path your_path --pretrained 1".

Evaluation

Since I just want to get used to Pytorch. I didn't prepare a evaluation method, you can make your own if you like.