Instructions to use ProbeX/Model-J__ResNet__model_idx_0467 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_0467 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_0467") 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_0467") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0467", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8acd4c89d489acb0534b4ca602747f82aa3410ebf02342837ccf3213f0f3b1ca
- Size of remote file:
- 5.37 kB
- SHA256:
- 1327f8a3d6d749552efe32738221e868182f02fc777500962e0b728667f52bca
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