https://github.com/enesdoruk/domatt-unet
Domain Adaptive Based Semantic Segmentation
https://github.com/enesdoruk/domatt-unet
cropandweed domainadaptation semantic-segmentation unet-pytorch unet-segmentation
Last synced: 7 months ago
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Domain Adaptive Based Semantic Segmentation
- Host: GitHub
- URL: https://github.com/enesdoruk/domatt-unet
- Owner: enesdoruk
- Created: 2024-04-25T11:49:07.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-05-28T10:42:12.000Z (over 1 year ago)
- Last Synced: 2024-05-29T01:12:41.297Z (over 1 year ago)
- Topics: cropandweed, domainadaptation, semantic-segmentation, unet-pytorch, unet-segmentation
- Language: Python
- Homepage:
- Size: 152 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Domain Adaptive U-Net: Domain Adaptive Semantic segmentation with PyTorch
Customized implementation of the [U-Net](https://arxiv.org/abs/1505.04597) with domain adaptive module in PyTorch for [Crop And Weed](https://github.com/cropandweed/cropandweed-dataset) from high definition images.
- [Quick start](#quick-start)
- [Description](#description)
- [Usage](#usage)
- [Training](#training)
- [Prediction](#prediction)
- [Weights & Biases](#weights--biases)
- [Data](#data)
## Quick start
### Without Docker
1. [Install CUDA](https://developer.nvidia.com/cuda-downloads)
2. [Install PyTorch 1.13 or later](https://pytorch.org/get-started/locally/)
3. Install dependencies
```bash
pip install -r requirements.txt
```
4. Download the data and run training:
For downloading data follow steps on [CropAndWeed](https://github.com/cropandweed/cropandweed-dataset)
```bash
python train.py --amp
```
## Description
This model was trained from scratch with 8k images and scored a [Dice coefficient](https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient) of 0.968423 on over 2k test images.
It can be easily used for multiclass segmentation, portrait segmentation, medical segmentation, ...
## Usage
**Note : Use Python 3.6 or newer**
### 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.
## Data
The Crop and Weed data is available on the [Crop And Weed](https://github.com/cropandweed/cropandweed-dataset).
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).
You can use your own dataset as long as you make sure it is loaded properly in `utils/data_loading.py`.
---
## Acknowledgments
- We thank the authors of [UNet](https://github.com/milesial/Pytorch-UNet) their open-source codes.