Image Segmentation
Transformers
PyTorch
modnet
feature-extraction
image-matting
background-removal
computer-vision
custom-architecture
custom_code
Instructions to use boopathiraj/MODNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use boopathiraj/MODNet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="boopathiraj/MODNet", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("boopathiraj/MODNet", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
MODNet - TorchScript Model
This TorchScript version of MODNet is provided by @yarkable from the community.
Please note that the PyTorch version required for this TorchScript export function is higher than the official MODNet code (torch>=1.2.0).
You can also download the TorchScript version of the official Image Matting Model from this link with the exextraction code dm9e.
To export the TorchScript version of MODNet (assuming you are currently in project root directory):
Download the pre-trained Image Matting Model from this link and put the model into the folder
MODNet/pretrained/.Ensure your PyTorch version >= 1.2.0.
Export the TorchScript version of MODNet by:
python -m torchscript.export_torchscript \ --ckpt-path=pretrained/modnet_photographic_portrait_matting.ckpt \ --output-path=pretrained/modnet_photographic_portrait_matting.torchscript