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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. 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.
- [Quick start using Docker](#quick-start-using-docker)
- [Description](#description)
- [Usage](#usage)
- [Docker](#docker)
- [Training](#training)
- [Prediction](#prediction)
- [Weights & Biases](#weights--biases)
- [Pretrained model](#pretrained-model)
- [Data](#data)
## Quick start using Docker ## Quick start using Docker
1. [Install Docker 19.03 or later:](https://docs.docker.com/get-docker/) 1. [Install Docker 19.03 or later:](https://docs.docker.com/get-docker/)
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``` ```
You can specify which model file to use with `--model MODEL.pth`. You can specify which model file to use with `--model MODEL.pth`.
### Weights & Biases ## Weights & Biases
The training progress can be visualized in real-time using [Weights & Biases](https://wandb.ai/). Loss curves, validation curves, weights and gradient histograms, as well as predicted masks are logged to the platform. The training progress can be visualized in real-time using [Weights & Biases](https://wandb.ai/). Loss curves, validation curves, weights and gradient histograms, as well as predicted masks are logged to the platform.
@ -124,7 +134,7 @@ When launching a training, a link will be printed in the console. Click on it to
by setting the `WANDB_API_KEY` environment variable. by setting the `WANDB_API_KEY` environment variable.
### Pretrained model ## Pretrained model
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: 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:
```python ```python
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## Data ## Data
The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data). The Carvana data is available on the [Kaggle website](https://www.kaggle.com/c/carvana-image-masking-challenge/data).
You can also download it using your Kaggle API key with: You can also download it using the helper script:
```shell script ```shell script
bash scripts/download_data.sh <username> <apikey> bash scripts/download_data.sh
``` ```
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. 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.