Instructions to use ProbeX/Model-J__ResNet__model_idx_0509 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_0509 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_0509") 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_0509") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0509", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92f7c6fe16a29e801fcd6d35e6bd49a519ae97e0b7c3ae8b090df8f2dedeedd0
- Size of remote file:
- 171 MB
- SHA256:
- 65ea9a5be539d4a3dabd65448c31261dcff28915ad9b55bc55085234f0845174
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