Instructions to use ProbeX/Model-J__ResNet__model_idx_0313 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_0313 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_0313") 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_0313") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0313", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 83e6a301de9bf0c90520e5592fc5040f814580695274e623af40b656b7b3c8f4
- Size of remote file:
- 5.37 kB
- SHA256:
- 4a851d08591208be24c2bd225bbf18357b3bab3e01078b5df33fad92c0127709
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