134 lines
5.2 KiB
Markdown
134 lines
5.2 KiB
Markdown
# U-Net: Semantic segmentation with PyTorch
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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.
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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.
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## Usage
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**Note : Use Python 3.6 or newer**
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### Docker
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A docker image containing the code and the dependencies is available on [DockerHub](https://hub.docker.com/repository/docker/milesial/unet).
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You can jump in the container with ([docker >=19.03](https://docs.docker.com/get-docker/)):
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```shell script
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docker run -it --rm --gpus all milesial/unet
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```
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### Training
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```shell script
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> python train.py -h
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usage: train.py [-h] [--epochs E] [--batch-size B] [--learning-rate LR]
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[--load LOAD] [--scale SCALE] [--validation VAL] [--amp]
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Train the UNet on images and target masks
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optional arguments:
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-h, --help show this help message and exit
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--epochs E, -e E Number of epochs
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--batch-size B, -b B Batch size
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--learning-rate LR, -l LR
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Learning rate
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--load LOAD, -f LOAD Load model from a .pth file
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--scale SCALE, -s SCALE
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Downscaling factor of the images
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--validation VAL, -v VAL
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Percent of the data that is used as validation (0-100)
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--amp Use mixed precision
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```
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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.
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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.
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### Prediction
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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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To predict a single image and save it:
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`python predict.py -i image.jpg -o output.jpg`
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To predict a multiple images and show them without saving them:
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`python predict.py -i image1.jpg image2.jpg --viz --no-save`
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```shell script
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> python predict.py -h
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usage: predict.py [-h] [--model FILE] --input INPUT [INPUT ...]
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[--output INPUT [INPUT ...]] [--viz] [--no-save]
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[--mask-threshold MASK_THRESHOLD] [--scale SCALE]
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Predict masks from input images
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optional arguments:
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-h, --help show this help message and exit
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--model FILE, -m FILE
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Specify the file in which the model is stored
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--input INPUT [INPUT ...], -i INPUT [INPUT ...]
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Filenames of input images
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--output INPUT [INPUT ...], -o INPUT [INPUT ...]
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Filenames of output images
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--viz, -v Visualize the images as they are processed
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--no-save, -n Do not save the output masks
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--mask-threshold MASK_THRESHOLD, -t MASK_THRESHOLD
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Minimum probability value to consider a mask pixel white
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--scale SCALE, -s SCALE
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Scale factor for the input images
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```
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You can specify which model file to use with `--model MODEL.pth`.
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### Weights & Biases
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The training progress can be visualized in real-time using [Weights & Biases](wandb.ai/). Loss curves, validation curves, weights and gradient histograms, as well as predicted masks are logged to the platform.
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When launching a training, a link will be printed in the console. Click on it to go to your dashboard. If you have an existing W&B account, you can link it
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by setting the `WANDB_API_KEY` environment variable.
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### Pretrained model
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A [pretrained model](https://github.com/milesial/Pytorch-UNet/releases/tag/v1.0) is available for the Carvana dataset. It can also be loaded from torch.hub:
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```python
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net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana')
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```
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The training was done with a 100% scale and bilinear upsampling.
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## Data
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The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data).
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You can also download it using your Kaggle API key with:
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```shell script
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bash scripts/download_data.sh <username> <apikey>
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```
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## Notes on memory
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The model has be trained from scratch on a GTX970M 3GB.
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Predicting images of 1918*1280 takes 1.5GB of memory.
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Training takes much approximately 3GB, so if you are a few MB shy of memory, consider turning off all graphical displays.
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This assumes you use bilinear up-sampling, and not transposed convolution in the model.
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## Convergence
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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).
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---
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Original paper by Olaf Ronneberger, Philipp Fischer, Thomas Brox: [https://arxiv.org/abs/1505.04597](https://arxiv.org/abs/1505.04597)
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