Instructions to use ProbeX/Model-J__ResNet__model_idx_0149 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_0149 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_0149") 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_0149") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0149", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 43cb67db9f32efcff59aa89f50d173de548636f8a0417c1f5d8e740a27538f64
- Size of remote file:
- 171 MB
- SHA256:
- c09e16fa62332cb05e74c9d72229310cf2ad0e20fa028b39bb8cd948111f2173
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