Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
SegformerForSemanticSegmentation
remove background
background
background-removal
Pytorch
vision
legal liability
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)# 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 results.png from algonia/rembg_clone: direct link, hf CLI and curl.
- Browser
- Download file 1.25 MB
-
https://huggingface.co/algonia/rembg_clone/resolve/main/results.png
- Command line
-
hf download hf://algonia/rembg_clone/results.png
-
curl -L -o results.png https://huggingface.co/algonia/rembg_clone/resolve/main/results.png
1.25 MB

- Xet hash:
- 6396fd8f2d043d7dda73005c99da62b5a92608bdca62ebf2353e5136f3595b87
- Size of remote file:
- 1.25 MB
- SHA256:
- 2b7f08fc4c09db56b516186c0629f72523a5cbe328beaedda8b36349af4b04bc
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