Instructions to use ahishamm/fine_tuned_efficient_sam with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ahishamm/fine_tuned_efficient_sam with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("mask-generation", model="ahishamm/fine_tuned_efficient_sam")# Load model directly from transformers import AutoProcessor, AutoModelForMaskGeneration processor = AutoProcessor.from_pretrained("ahishamm/fine_tuned_efficient_sam") model = AutoModelForMaskGeneration.from_pretrained("ahishamm/fine_tuned_efficient_sam", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f2b531d1e041deb4ae10c0fb0649ace61a9ab810d0196220bd169cf4e3491e94
- Size of remote file:
- 375 MB
- SHA256:
- 4cf95e1fef08466061f565330097bf2b8385fc1314db5d4c54057e53cc8e3dfd
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