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)# Load model directly from transformers import AutoModelForImageSegmentation model = AutoModelForImageSegmentation.from_pretrained("OwlMaster/FixRM", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle

- Xet hash:
- 3ccf63aff48f3a1bce6178e3b29d3a2dd6f69d34378b48bff8a9e61f8fc22bd6
- Size of remote file:
- 2.16 MB
- SHA256:
- 43a9453f567d9bff7fe4481205575bbf302499379047ee6073247315452ba8fb
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