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98 lines
4.1 KiB
Markdown
98 lines
4.1 KiB
Markdown
# UNet: 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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The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data).
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## Usage
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**Note : Use Python 3**
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### Prediction
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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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(default: MODEL.pth)
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--input INPUT [INPUT ...], -i INPUT [INPUT ...]
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filenames of input images (default: None)
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--output INPUT [INPUT ...], -o INPUT [INPUT ...]
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Filenames of ouput images (default: None)
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--viz, -v Visualize the images as they are processed (default:
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False)
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--no-save, -n Do not save the output masks (default: False)
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--mask-threshold MASK_THRESHOLD, -t MASK_THRESHOLD
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Minimum probability value to consider a mask pixel
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white (default: 0.5)
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--scale SCALE, -s SCALE
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Scale factor for the input images (default: 0.5)
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```
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You can specify which model file to use with `--model MODEL.pth`.
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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] [-e E] [-b [B]] [-l [LR]] [-f LOAD] [-s SCALE] [-v VAL]
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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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-e E, --epochs E Number of epochs (default: 5)
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-b [B], --batch-size [B]
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Batch size (default: 1)
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-l [LR], --learning-rate [LR]
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Learning rate (default: 0.1)
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-f LOAD, --load LOAD Load model from a .pth file (default: False)
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-s SCALE, --scale SCALE
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Downscaling factor of the images (default: 0.5)
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-v VAL, --validation VAL
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Percent of the data that is used as validation (0-100)
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(default: 15.0)
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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.
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## Tensorboard
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You can visualize in real time the train and test losses, along with the model predictions with tensorboard:
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`tensorboard --logdir=runs`
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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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---
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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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