Instructions to use ProbeX/Model-J__ResNet__model_idx_0537 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_0537 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_0537") 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_0537") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0537") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 3def3f1b64069d9f5b9c152d98ce7f169d51963e7061716c112de6e14e8e404a
- Size of remote file:
- 5.37 kB
- SHA256:
- ab6befca8bd78ac5d1dea68c47fa2159478fc8d63392fcb9847643282d17b686
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