Instructions to use ProbeX/Model-J__ResNet__model_idx_0928 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_0928 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_0928") 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_0928") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0928", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d6a32c0d8b364e1b1ab4c67f562f9abc7ae0d0bdf75d707bf6aec2a4eb1b6669
- Size of remote file:
- 171 MB
- SHA256:
- b435ee932bc7cd73da3f0b1fbcb6a8b7be1eb78869a1272c47bc4b70695afb2b
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