Image Segmentation
BiRefNet
Safetensors
Transformers
Transformers.js
background-removal
mask-generation
Dichotomous Image Segmentation
Camouflaged Object Detection
Salient Object Detection
pytorch_model_hub_mixin
model_hub_mixin
custom_code
Instructions to use lygitdata/BiRefNet_garmentiq_backup with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- BiRefNet
How to use lygitdata/BiRefNet_garmentiq_backup with BiRefNet:
# Option 1: use with transformers from transformers import AutoModelForImageSegmentation birefnet = AutoModelForImageSegmentation.from_pretrained("lygitdata/BiRefNet_garmentiq_backup", trust_remote_code=True)# Option 2: use with BiRefNet # Install from https://github.com/ZhengPeng7/BiRefNet from models.birefnet import BiRefNet model = BiRefNet.from_pretrained("lygitdata/BiRefNet_garmentiq_backup") - Transformers
How to use lygitdata/BiRefNet_garmentiq_backup with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="lygitdata/BiRefNet_garmentiq_backup", trust_remote_code=True)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("lygitdata/BiRefNet_garmentiq_backup", trust_remote_code=True, dtype="auto") - Transformers.js
How to use lygitdata/BiRefNet_garmentiq_backup with Transformers.js:
// npm i @huggingface/transformers import { pipeline } from '@huggingface/transformers'; // Allocate pipeline const pipe = await pipeline('image-segmentation', 'lygitdata/BiRefNet_garmentiq_backup'); - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 796e4ababf8368833ae155c9063ce8a6bce38bc1b3783682e62c87f789cddcbe
- Size of remote file:
- 444 MB
- SHA256:
- 9ab37426bf4de0567af6b5d21b16151357149139362e6e8992021b8ce356a154
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