基于Pytorch的UNet语义分割模型与代码【模型在FloodNet数据集上进行了训练，mIOU在0.83左右】

# U-Net: Semantic segmentation with PyTorch <a href="#"><img src="https://img.shields.io/github/workflow/status/milesial/PyTorch-UNet/Publish%20Docker%20image?logo=github&style=for-the-badge" /></a> <a href="https://hub.docker.com/r/milesial/unet"><img src="https://img.shields.io/badge/docker%20image-available-blue?logo=Docker&style=for-the-badge" /></a> <a href="https://pytorch.org/"><img src="https://img.shields.io/badge/PyTorch-v1.9.0-red.svg?logo=PyTorch&style=for-the-badge" /></a> <a href="#"><img src="https://img.shields.io/badge/python-v3.6+-blue.svg?logo=python&style=for-the-badge" /></a>  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](#quick-start) - [Without Docker](#without-docker) - [With Docker](#with-docker) - [Description](#description) - [Usage](#usage) - [Docker](#docker) - [Training](#training) - [Prediction](#prediction) - [Weights & Biases](#weights--biases) - [Pretrained model](#pretrained-model) - [Data](#data) ## Quick start ### Without Docker 1. [Install CUDA](https://developer.nvidia.com/cuda-downloads) 2. [Install PyTorch](https://pytorch.org/get-started/locally/) 3. Install dependencies ```bash pip install -r requirements.txt ``` 4. Download the data and run training: ```bash bash scripts/download_data.sh python train.py --amp ``` ### With Docker 1. [Install Docker 19.03 or later:](https://docs.docker.com/get-docker/) ```bash curl https://get.docker.com | sh && sudo systemctl --now enable docker ``` 2. [Install the NVIDIA container toolkit:](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html) ```bash distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \ && curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add - \ && curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list sudo apt-get update sudo apt-get install -y nvidia-docker2 sudo systemctl restart docker ``` 3. [Download and run the image:](https://hub.docker.com/repository/docker/milesial/unet) ```bash sudo docker run --rm --shm-size=8g --ulimit memlock=-1 --gpus all -it milesial/unet ``` 4. Download the data and run training: ```bash bash scripts/download_data.sh python train.py --amp ``` ## Description This model was trained from scratch with 5k images 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. It can be easily used for multiclass segmentation, portrait segmentation, medical segmentation, ... ## Usage **Note : Use Python 3.6 or newer** ### Docker A docker image containing the code and the dependencies is available on [DockerHub](https://hub.docker.com/repository/docker/milesial/unet). You can download and jump in the container with ([docker >=19.03](https://docs.docker.com/get-docker/)): ```console docker run -it --rm --shm-size=8g --ulimit memlock=-1 --gpus all milesial/unet ``` ### Training ```console > python train.py -h usage: train.py [-h] [--epochs E] [--batch-size B] [--learning-rate LR] [--load LOAD] [--scale SCALE] [--validation VAL] [--amp] Train the UNet on images and target masks optional arguments: -h, --help show this help message and exit --epochs E, -e E Number of epochs --batch-size B, -b B Batch size --learning-rate LR, -l LR Learning rate --load LOAD, -f LOAD Load model from a .pth file --scale SCALE, -s SCALE Downscaling factor of the images --validation VAL, -v VAL Percent of the data that is used as validation (0-100) --amp Use mixed precision ``` By default, the `scale` is 0.5, so if you wish to obtain better results (but use more memory), set it to 1. Automatic mixed precision is also available with the `--amp` flag. [Mixed precision](https://arxiv.org/abs/1710.03740) allows the model to use less memory and to be faster on recent GPUs by using FP16 arithmetic. Enabling AMP is recommended. ### Prediction After training your model and saving it to `MODEL.pth`, you can easily test the output masks on your images via the CLI. To predict a single image and save it: `python predict.py -i image.jpg -o output.jpg` To predict a multiple images and show them without saving them: `python predict.py -i image1.jpg image2.jpg --viz --no-save` ```console > python predict.py -h usage: predict.py [-h] [--model FILE] --input INPUT [INPUT ...] [--output INPUT [INPUT ...]] [--viz] [--no-save] [--mask-threshold MASK_THRESHOLD] [--scale SCALE] Predict masks from input images optional arguments: -h, --help show this help message and exit --model FILE, -m FILE Specify the file in which the model is stored --input INPUT [INPUT ...], -i INPUT [INPUT ...] Filenames of input images --output INPUT [INPUT ...], -o INPUT [INPUT ...] Filenames of output images --viz, -v Visualize the images as they are processed --no-save, -n Do not save the output masks --mask-threshold MASK_THRESHOLD, -t MASK_THRESHOLD Minimum probability value to consider a mask pixel white --scale SCALE, -s SCALE Scale factor for the input images ``` You can specify which model file to use with `--model MODEL.pth`. ## 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. 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 by setting the `WANDB_API_KEY` environment variable. If not, it will create an anonymous run which is automatically deleted after 7 days. ## Pretrained model A [pretrained model](https://github.com/milesial/Pytorch-UNet/releases/tag/v3.0) is available for the Carvana dataset. It can also be loaded from torch.hub: ```python net = torch.hub.load('milesial/Pytorch-UNet', 'unet_carvana', pretrained=True, scale=0.5) ``` Available scales are 0.5 and 1.0. ## 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 the helper script: ``` bash scripts/download_data.sh ``` The input images and target masks should be in the `data/imgs` and `data/masks` folders respectively (note that the `imgs` and `masks` folder should not contain any sub-folder or any other files, due to the greedy data-loader). For Carvana, images are RGB and masks are black and white. You can 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: [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://arxiv.org/abs/1505.04597)