Pytorch implementation of Human(person) segmentation from RGB images.
Dataset is available in https://supervise.ly/explore/projects/supervisely-person-dataset-23304/datasets Please make sure to arrange the Dataset tree as follows. dataset_dir(str) : path to the dataset(root dir) and arranged as follows.
├── Dataset
│ ├── sample.png
│ ├──images
│ ├── sample.png
│ ├── masks
│ ├── id[0].png
└── id[i].png
- Python3
- Pytorch 1.1.0
jupyter notebook(Unet_Evaluation.ipynb) for data loader and model prediction is provided.
python train.py
If you want help - python train.py --help
A new feature has added with the existing Segmentation Model. We use the UNET model to transfer the input image colorful background to Grey . Since our model is trained only for person classes, demos are limited to input images with at least one person. I have provided a demo notebook to guide you to achieve this. Here are some demo samples. .
Before you start, pleae put the pretrained model in the main folder.
You can find the pretrained model here : https://drive.google.com/file/d/1iaJA5AvNmAuFUv1HbG0_gWVZWA-ZN4La/view?usp=sharing