Instructions to use ProbeX/Model-J__ResNet__model_idx_0393 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_0393 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_0393") 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_0393") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0393") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ba57ffc1384133960d6fdc9ba369e3560372146080faddcb2ac28f9e6e69049
- Size of remote file:
- 5.37 kB
- SHA256:
- abf0458c3281a43767b28a204fc46d16d9779b1372eb7cf4cca0de5c876090c4
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