Instructions to use ProbeX/Model-J__ResNet__model_idx_0874 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_0874 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_0874") 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_0874") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0874", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfe842a46111d6db0a87adf2c8675d1f3ea0e05bfff167592a3bbc57cc00eeae
- Size of remote file:
- 171 MB
- SHA256:
- 3847c289ba23e87ef95d9aeb92320304cf9413d00e8830f1d96b56a5a4a404b6
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