Instructions to use ProbeX/Model-J__ResNet__model_idx_0161 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_0161 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_0161") 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_0161") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0161") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 033d69c283c716211ecbe001c3d781544de316bde1da42a73dafa82bdface456
- Size of remote file:
- 5.37 kB
- SHA256:
- 41d73bb12d086798474222bde036b1ee9919d6bbe2c34a0cb5df4e203ec44381
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