This repository contains code for a multiple classification image segmentation model based on UNet and UNet++
make sure to put the files as the following structure:
data
├── images
| ├── 0a7e06.jpg
│ ├── 0aab0a.jpg
│ ├── 0b1761.jpg
│ ├── ...
|
└── masks
├── 0a7e06.png
├── 0aab0a.png
├── 0b1761.png
├── ...
mask is a single-channel category index. For example, your dataset has three categories, mask should be 8-bit images with value 0,1,2 as the categorical value, this image looks black.
You can download the demo dataset from here to data/
python train.pypython inference.py -m ./data/checkpoints/epoch_10.pth -i ./data/test/input -o ./data/test/output
If you want to highlight your mask with color, you can
python inference_color.py -m ./data/checkpoints/epoch_10.pth -i ./data/test/input -o ./data/test/outputYou can visualize in real time the train and val losses, along with the model predictions with tensorboard:
tensorboard --logdir=runs
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent