Instructions to use sna89/segmentation_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sna89/segmentation_model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-segmentation", model="sna89/segmentation_model")# Load model directly from transformers import AutoImageProcessor, MaskFormerForInstanceSegmentation processor = AutoImageProcessor.from_pretrained("sna89/segmentation_model") model = MaskFormerForInstanceSegmentation.from_pretrained("sna89/segmentation_model", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- de8e881b64b6ec1c3bbeac0c807c044e0a5dfbdeedac8c2b451c2e6e8b8c8691
- Size of remote file:
- 411 MB
- SHA256:
- 075df80e373bdb4527903246d5bc0584da87e83faf087f0533e20eb3d0434b0d
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