Instructions to use ProbeX/Model-J__ResNet__model_idx_0793 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_0793 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_0793") 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_0793") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0793", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 88a73f2b93e8080708f6d7d04c2626d3de61866b01709e6b37677897363f19d3
- Size of remote file:
- 5.37 kB
- SHA256:
- a26cb57486dee3f0038fabf4e3ed85cf53373f5c4f996e99989b9160b12f908d
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