Image Segmentation
Transformers
PyTorch
ONNX
Safetensors
SegformerForSemanticSegmentation
remove background
background
background-removal
Pytorch
vision
legal liability
custom_code
Instructions to use OwlMaster/FixRM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OwlMaster/FixRM with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="OwlMaster/FixRM", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("OwlMaster/FixRM", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.pth from OwlMaster/FixRM: direct link, hf CLI and curl.
- Browser
- Download file 177 MB
-
https://huggingface.co/OwlMaster/FixRM/resolve/main/model.pth
- Command line
-
hf download hf://OwlMaster/FixRM/model.pth
-
curl -L -o model.pth https://huggingface.co/OwlMaster/FixRM/resolve/main/model.pth
177 MB
- Xet hash:
- 4e4a841a10e4af19d14ba1eea5fbfe603cc33a164b8073792ed5a19ebd1b635a
- Size of remote file:
- 177 MB
- SHA256:
- 893c16c340b1ddafc93e78457a4d94190da9b7179149f8574284c83caebf5e8c
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