Instructions to use wuchendi/MODNet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers.js
How to use wuchendi/MODNet with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'wuchendi/MODNet');
Commit History
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docs(README): Updated MODNet-related links
- Updated the MODNet repository link to the GitHub address - Adjusted the format of the MODNet Spaces link - Fixed the position of the SwanLab/MODNet link0a393d9 -
docs(README): Update MODNet-related links and installation instructions
- Added Spaces link - Improved the formatting of Repository and SwanLab/MODNet links - Changed NPM to PNPM in the installation instructionsfd4f18c -
docs(README): Add MODNet model usage instructions and example code
- Added complete example code for portrait segmentation using Transformers.js - Displayed example results of input images and output masks22b45c9
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feat: Add MODNet model configuration and quantization settings
- Added config.json file to configure MODNet model type and data type - Added ONNX model files, including FP32, FP16, and quantized versions - Added preprocessor configuration and quantization configuration files - Removed unnecessary requirements.txt file - Deleted test and training scripts to simplify project structureda8a9cc -
docs(README): Update MODNet model information
- Added model name and tags - Updated license information - Added model description3bc2007 -
ci: Add swanlab initialization and training logging
- Added requirements.txt file to list project dependencies - Added train.py file to implement basic training logging functionality0c6c59a -
Create test.py
Verified signaturee7a6c95 -
initial commit
Verified signaturef59dca1