Instructions to use ProbeX/Model-J__ResNet__model_idx_0994 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ProbeX/Model-J__ResNet__model_idx_0994 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="ProbeX/Model-J__ResNet__model_idx_0994") pipe("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hub/parrots.png")# Load model directly from transformers import AutoImageProcessor, AutoModelForImageClassification processor = AutoImageProcessor.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0994") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0994") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 061983a95db63b44de06d5b571e293a44a9aba580b7ada4e9a08d4a026ce31ef
- Size of remote file:
- 5.37 kB
- SHA256:
- 2345cdb4f4a4c99ff61f5d46d2d3f3a133b688d942a8a2c9f05229a63cb8b923
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