https://github.com/nvti/resnet
An implementation of ResNet in tensorflow 2.0
https://github.com/nvti/resnet
computer-vision resnet tensorflow2
Last synced: about 2 months ago
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An implementation of ResNet in tensorflow 2.0
- Host: GitHub
- URL: https://github.com/nvti/resnet
- Owner: nvti
- License: gpl-3.0
- Created: 2021-06-24T16:08:07.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-07-01T15:08:35.000Z (about 5 years ago)
- Last Synced: 2025-01-25T18:30:58.698Z (over 1 year ago)
- Topics: computer-vision, resnet, tensorflow2
- Language: Python
- Homepage:
- Size: 212 KB
- Stars: 4
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
# Implementation of ResNet in tensorflow 2
My implementation for paper [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385), using [tensorflow 2](https://www.tensorflow.org/)
> ResNet model architecture (figure from the paper)
## Pre-requirement
Install dependence package with pip:
```
pip install -r requirements.txt
```
## Setup dataset folder
This library use `image_dataset_from_directory` API from Tensorflow 2.0 to load images. You can read more about this API [here](https://www.tensorflow.org/api_docs/python/tf/keras/preprocessing/image_dataset_from_directory)
Dataset directory structure:
```
main_directory/
...class_a/
......a_image_1.jpg
......a_image_2.jpg
...class_b/
......b_image_1.jpg
......b_image_2.jpg
```
## Training
I create `train.py` for training model.
```
usage: train.py [-h] [--model MODEL] [--classes CLASSES] [--lr LR] [--batch-size BATCH_SIZE] [--epochs EPOCHS] [--image-size IMAGE_SIZE] [--image-channels IMAGE_CHANNELS]
[--train-folder TRAIN_FOLDER] [--valid-folder VALID_FOLDER] [--model-folder MODEL_FOLDER]
optional arguments:
-h, --help show this help message and exit
--model MODEL Type of ResNet model, valid option: resnet18, resnet34, resnet50, resnet101, resnet152
--classes CLASSES Number of classes
--lr LR Learning rate
--batch-size BATCH_SIZE
Batch size
--epochs EPOCHS Number of training epoch
--image-size IMAGE_SIZE
Size of input image
--image-channels IMAGE_CHANNELS
Number channel of input image
--train-folder TRAIN_FOLDER
Where training data is located
--valid-folder VALID_FOLDER
Where validation data is located
--model-folder MODEL_FOLDER
Folder to save trained model
```
An example command for training:
```
python train.py --model resnet18 --epochs 10 --num-classes 2
```
After training successfully, your model will be saved to `model-folder`, default is `./output`
## Prediction
After your model is trained successfully, you can test your model with `predict.py` script:
```
python predict.py --test-image ./test.png --image-size=28
```