Instructions to use ProbeX/Model-J__ResNet__model_idx_0082 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_0082 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_0082") 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_0082") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0082", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- e93546e033d7c56384903ef4fe20eb494aab36216846eb23b8a52f7b90096c4f
- Size of remote file:
- 5.37 kB
- SHA256:
- 979585ec5f4a2e1f36ac4f4168e97901abb455dfd6554c3c86cbf608bc7c395d
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