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https://github.com/calmiLovesAI/TensorFlow2.0_ResNet
A ResNet(ResNet18, ResNet34, ResNet50, ResNet101, ResNet152) implementation using TensorFlow-2.0.
https://github.com/calmiLovesAI/TensorFlow2.0_ResNet
Last synced: 3 months ago
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A ResNet(ResNet18, ResNet34, ResNet50, ResNet101, ResNet152) implementation using TensorFlow-2.0.
- Host: GitHub
- URL: https://github.com/calmiLovesAI/TensorFlow2.0_ResNet
- Owner: calmiLovesAI
- License: mit
- Created: 2019-07-16T09:23:52.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2021-08-28T02:52:44.000Z (about 3 years ago)
- Last Synced: 2024-07-28T16:13:34.379Z (4 months ago)
- Language: Python
- Homepage:
- Size: 16.1 MB
- Stars: 313
- Watchers: 5
- Forks: 100
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Tensorflow2 - calmisential/TensorFlow2.0_ResNet
README
# 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](https://github.com/calmisential/TensorFlow2.0_ResNet)
+ [InceptionV3](https://github.com/calmisential/TensorFlow2.0_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