https://github.com/monatis/mobilenetv2-tf2
A reusable TensorFlow 2.0 and Keras implementation of MobileNetV2
https://github.com/monatis/mobilenetv2-tf2
Last synced: 10 months ago
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A reusable TensorFlow 2.0 and Keras implementation of MobileNetV2
- Host: GitHub
- URL: https://github.com/monatis/mobilenetv2-tf2
- Owner: monatis
- Created: 2019-08-02T19:38:12.000Z (about 7 years ago)
- Default Branch: master
- Last Pushed: 2022-02-11T02:55:23.000Z (over 4 years ago)
- Last Synced: 2023-03-03T13:40:47.461Z (over 3 years ago)
- Language: Python
- Size: 18.6 KB
- Stars: 13
- Watchers: 3
- Forks: 4
- Open Issues: 7
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Metadata Files:
- Readme: readme.md
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README
# mobilenetv2-tf2
A TensorFlow 2.0 implementation of [Inverted Residuals and Linear Bottlenecks Mobile Networks for
Classification, Detection and Segmentation](https://arxiv.org/abs/1801.04381), aka MobileNetV2.
## Motivation
MobileNetV2 is still one of the most efficient architectures for image classification. Considering that TensorFlow 2.0 has already hit version beta1, I think that a flexible and reusable implementation of MobileNetV2 in TF 2.0 might be useful for practitioners.
## Implementation
I implemented a running mean and standard deviation calculation with [Welford algorithm](https://www.johndcook.com/blog/standard_deviation/), which eliminates the problem of loading the whole dataset into the memory. `Normalizer` class, calculating the mean and standard deviation, is also used as a `preprocessing_function` argument to `tf.keras.preprocessing.image.ImageDataGenerator`.
## Install
1. `conda create -n mobilenetv2 python=3.6.8`
2. `conda activate mobilenetv2`
3. `git clone https://github.com/monatis/mobilenetv2-tf2.git`
4. `cd mobilenetv2-tf2`
5. `python -m pip install -r requirements.gpu.txt` # Change to `requirements.cpu.txt` if you're not using GPU.
## Usage
`train_dir` and `validation_dir` directories should contain a subdirectory for each class in the dataset. Then run:
- `python train.py --train_dir /path/to/training/images --validation_dir /path/to/validation/images`
- See `model/` directory for training output.
run `python train.py --help` to see all the options.
## Roadmap
- [x] Share model architecture and a training script.
- [x] Implement export to saved model.
- [x] Implement command line arguments to configure data augmentation.
- [ ] Share an inference script.
- [x] Implement mean and STD normalization.
- [ ] Implement confusion matrix.
- [ ] Implement export to TFLite for model inference.
- [ ] Share an example Android app using the exported TFLite model.
## License
MIT