Instructions to use ProbeX/Model-J__ResNet__model_idx_0896 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_0896 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_0896") 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_0896") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0896", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 985c3e160a8c42339374402f39a2939c607428ef26d8ee82fe502ed1b23aefad
- Size of remote file:
- 171 MB
- SHA256:
- 8ba912fc8daa95d91d0e710388083a9503301f30115ff3606022a9b1447a40e6
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