Instructions to use ProbeX/Model-J__ResNet__model_idx_0002 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_0002 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_0002") 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_0002") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0002", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e3961b4264bec3fd8c226b6c20f6116b0cef1d4f3fcae8227c2a68fd7968e3d5
- Size of remote file:
- 5.37 kB
- SHA256:
- c0ff093139449447bf35f4ff2414b97c1c4cbb92f4b11f27c8cb40595bbc7de4
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