Instructions to use ProbeX/Model-J__ResNet__model_idx_0112 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_0112 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_0112") 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_0112") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0112") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 689dcf9387f261a41c8e1c7ceed956a7758bd95ad6b268fbaaa08b1305302d3a
- Size of remote file:
- 5.37 kB
- SHA256:
- 9860fc371ccc2986d64fc82ed0116b44116ab502394b571f3eec16cf23d64a24
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