Instructions to use ProbeX/Model-J__ResNet__model_idx_0079 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_0079 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_0079") 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_0079") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0079", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 0b1b29f9373c56b8eb3b4c3cacdb9f6f2dc1fc144d0af27e02a450b16dd83702
- Size of remote file:
- 5.37 kB
- SHA256:
- 28e0eca1b4b8af932b104cbd2ac5ac2a3878d0d96f41162f26f2d35ca9556666
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