Instructions to use ProbeX/Model-J__ResNet__model_idx_0451 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_0451 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_0451") 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_0451") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0451", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1d9e3e4683f16feac9404e6806684aa42884578558d9abedec53f75df50104f3
- Size of remote file:
- 171 MB
- SHA256:
- 91c1388cad11d7431220858aac3c35d814d1c5660b6a6a5cf18dcb9983af6e5f
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