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:
- 53dbd964dd5c0816b875c0a109bc9469ea127f0e9fe5f9b9cdf9df0b793f7cb8
- Size of remote file:
- 44.4 MB
- SHA256:
- a6648479275dfd0ede0f3a8abc20aa5c437b394681b05e5af6d268250aaf40f3
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