Instructions to use ProbeX/Model-J__ResNet__model_idx_0759 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_0759 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_0759") 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_0759") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0759") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- b380c895f21ca36f78d79e7c983f535214c17fea837c51a7f22704b2bdd0eb01
- Size of remote file:
- 5.37 kB
- SHA256:
- b607e06ac5baa6bde763386a56064a81729492c10736a74c2b515718155587f0
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