Instructions to use ProbeX/Model-J__ResNet__model_idx_0620 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_0620 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_0620") 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_0620") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0620") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 248bb5c302c9e01763a7e03ad600ee7dc0abf74fa5399e48658d17bf3c7e72f7
- Size of remote file:
- 5.37 kB
- SHA256:
- 34ac300468b4eb4c3aeb249ac5b495c95745a55625787e94494d5e8f223f802c
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