TensorFlow2.0_ResNet

A ResNet(ResNet18, ResNet34, ResNet50, ResNet101, ResNet152) implementation using TensorFlow-2.0

See https://github.com/calmisential/Basic_CNNs_TensorFlow2.0 for more CNNs.

Train

1. Requirements:

• Python >= 3.6
• Tensorflow == 2.0.0

2. To train the ResNet on your own dataset, you can put the dataset under the folder original dataset, and the directory should look like this:

|——original dataset
   |——class_name_0
   |——class_name_1
   |——class_name_2
   |——class_name_3

3. Run the script split_dataset.py to split the raw dataset into train set, valid set and test set.
4. Change the corresponding parameters in config.py.
5. Run train.py to start training.

Evaluate

Run evaluate.py to evaluate the model's performance on the test dataset.

The networks I have implemented with tensorflow2.0:

• ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
• InceptionV3

References

1. The original paper: https://arxiv.org/abs/1512.03385
2. The TensorFlow official tutorials: https://tensorflow.google.cn/beta/tutorials/quickstart/advanced