Instructions to use ProbeX/Model-J__ResNet__model_idx_0844 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_0844 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_0844") 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_0844") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0844", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 80265350acda3d1cf102c1025cbd55c7173d4988b44d663c26b9c18f76230e10
- Size of remote file:
- 171 MB
- SHA256:
- cb8c3383e4c79a91f21eec237ee2a41694011e983ab0364efca9e7589dd1071a
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