Instructions to use ProbeX/Model-J__ResNet__model_idx_0657 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_0657 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_0657") 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_0657") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0657", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e21b4be2b55dc7aa38c39cde3ede0999af144e3745a149c1072eee3202a64c6b
- Size of remote file:
- 5.37 kB
- SHA256:
- 7ddfd58c918c488f9ae6524da9fd2e98086a37f89583f8e061b6a634d45eb897
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