Instructions to use ProbeX/Model-J__ResNet__model_idx_0331 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_0331 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_0331") 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_0331") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0331") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 118e8530f0ff20f5cb92b00fb4a2c3a40b3d4e1efb6f1059a2d86977286eb5a3
- Size of remote file:
- 171 MB
- SHA256:
- e02153bf2bcbb71cd853a0af8f887f4cab706f8c8db74c6366a368d53ed8c757
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