Instructions to use ProbeX/Model-J__ResNet__model_idx_0980 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_0980 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_0980") 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_0980") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0980") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eecf0eed356b35d9f8e50781cbb813d9267a2a643b696df66d2235b63772891b
- Size of remote file:
- 5.37 kB
- SHA256:
- 5656b79f1a8f79a9fb64b1fdc5dd3ba6a1693df120f11dbbf920481c1eedc74b
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