TF2CIFAR10

Introduction

I trained CIFAR10 dataset with TensorFlow2, it is very easy to build the project environment by using TensorFlow2’s docker container.
You can download it form here .

If you find this project useful and using it in your work please cite this implementation, thanks.

Requirements

• TensorFlow2.0
• Python3
• opencv-python
• tqdm

Accuracy

Usage

1. Download CIFAR10 dataset in Google Drive or Baidu Yun 85r9 and
   unzip it. Then, put them under directory

    ./cifar/original

2. Convert CIFAR10 dataset by run parse.py

    python3 ./dataset/parse.py

3. Train the model using tain.py, you need to modify train.py to choose a net you want to train.

    python3 train.py

   Details of training

• optimizer:SGD
• lr:1e-1
• lr range:[(150, 1e-2), (250, 1e-3), (350, 1e-4)]
• weight decay:5e-4
• label smoothing:2e-1

Please read the config.py for more details.

Pretrained models for download

You can test the accuracy of CIFAR10 by my models.

Reference

Keras-DualPathNetworks

pytorch-cifar

Trains a ResNet on the CIFAR10 dataset