Instructions to use ProbeX/Model-J__ResNet__model_idx_0639 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_0639 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_0639") 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_0639") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0639", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- d0658146e33983d6431fa3bb09289b30755e28669101186c852b056ae309ff72
- Size of remote file:
- 5.37 kB
- SHA256:
- a41968eb118e0292ce45ba227b92eb9bc5e4a9a2b562bf496b76f4891103afb7
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