A TensorFlow_2.0 implementation of InceptionV3.

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

1. Requirements:

• Python >= 3.6
• Tensorflow == 2.0.0-rc2

1. To train the InceptionV3 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

1. Run the script split_dataset.py to split the raw dataset into train set, valid set and test set. The dataset directory will be like this:

|——dataset
  |——train
       |——class_name_1
       |——class_name_2
       ......
       |——class_name_n
  |——valid
       |——class_name_1
       |——class_name_2
       ......
       |——class_name_n
  |—-test
       |——class_name_1
       |——class_name_2
       ......
       |——class_name_n

1. Change the corresponding parameters in config.py.
2. Run train.py to start training.

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

• ResNet34, ResNet50, ResNet101, ResNet152
• InceptionV3

1. The original paper :https://arxiv.org/abs/1512.00567
2. Google official implementation of InceptionV3 (TensorFlow 1.x): https://github.com/tensorflow/models/blob/master/research/slim/nets/inception_v3.py
3. https://www.jianshu.com/p/3bbf0675cfce
4. Official PyTorch implementation of InceptionV3 : https://github.com/pytorch/vision/blob/master/torchvision/models/inception.py