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Customized implementation of the [U-Net](https://arxiv.org/abs/1505.04597) in PyTorch for Kaggle's [Carvana Image Masking Challenge](https://www.kaggle.com/c/carvana-image-masking-challenge) from high definition images.
This model was trained from scratch with 5000 images (no data augmentation) and scored a [dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) of 0.988423 (511 out of 735) on over 100k test images. This score could be improved with more training, data augmentation, fine tuning, playing with CRF post-processing, and applying more weights on the edges of the masks.
This model was trained from scratch with 5000 images (no data augmentation) and scored a [dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) of 0.988423 on over 100k test images. This score could be improved with more training, data augmentation, fine-tuning, CRF post-processing, and applying more weights on the edges of the masks.
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### Docker
A docker image containing the code and the dependencies is available on [DockerHub](https://hub.docker.com/repository/docker/milesial/unet).
You can jump in the container with ([docker >=19.03](https://docs.docker.com/get-docker/)):
You can **download and jump in the container** with ([docker >=19.03](https://docs.docker.com/get-docker/)):
```shell script
docker run -it --rm --gpus all milesial/unet
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```
By default, the `scale` is 0.5, so if you wish to obtain better results (but use more memory), set it to 1.
The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively. For Carvana, images are RGB and masks are black and white.
Automatic mixed precision is aso available with the `--amp` flag. Mixed precision allows the model to use less memory and to be faster on recent GPUs.
### Prediction
After training your model and saving it to `MODEL.pth`, you can easily test the output masks on your images via the CLI.
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bash scripts/download_data.sh <username> <apikey>
```
## Notes on memory
The model has be trained from scratch on a GTX970M 3GB.
Predicting images of 1918*1280 takes 1.5GB of memory.
Training takes much approximately 3GB, so if you are a few MB shy of memory, consider turning off all graphical displays.
This assumes you use bilinear up-sampling, and not transposed convolution in the model.
## Convergence
See a reference training run with the Caravana dataset on [TensorBoard.dev](https://tensorboard.dev/experiment/1m1Ql50MSJixCbG1m9EcDQ/#scalars&_smoothingWeight=0.6) (only scalars are shown currently).
The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively. For Carvana, images are RGB and masks are black and white.
You can also use your own dataset as long as you make sure it is loaded properly in `utils/data_loading.py`.
---
Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: [https://arxiv.org/abs/1505.04597](https://arxiv.org/abs/1505.04597)
Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox:
[U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)
![network architecture](https://i.imgur.com/jeDVpqF.png)