Instructions to use ProbeX/Model-J__ResNet__model_idx_0641 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_0641 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_0641") 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_0641") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0641", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f3eae0ede1b75e209704ffdf69541677d25ad8d9bcae0576b3c1f1b7bda41f64
- Size of remote file:
- 171 MB
- SHA256:
- 78364bde930273e6cbf3d682b75cedb1abffcdda51d1e4200738d4c2cf198d9f
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