Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
SegformerForSemanticSegmentation
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background
background-removal
Pytorch
vision
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custom_code
Instructions to use algonia/rembg_clone with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use algonia/rembg_clone with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="algonia/rembg_clone", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("algonia/rembg_clone", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from algonia/rembg_clone: direct link, hf CLI and curl.
- Browser
- Download file 177 MB
-
https://huggingface.co/algonia/rembg_clone/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://algonia/rembg_clone/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/algonia/rembg_clone/resolve/main/pytorch_model.bin
177 MB
- Xet hash:
- 87b8cb522d9cebcd618ce36661e096acdc34d5809fffc04b839787ec32a44749
- Size of remote file:
- 177 MB
- SHA256:
- 59569acdb281ac9fc9f78f9d33b6f9f17f68e25086b74f9025c35bb5f2848967
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