Instructions to use ProbeX/Model-J__ResNet__model_idx_0721 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_0721 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_0721") 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_0721") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0721", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30e0fda9b636714011af53ee5d9377124650fc093f0f3d623482275aaffaa17d
- Size of remote file:
- 5.37 kB
- SHA256:
- 943e362f59d6202ad701d9f252148001f4de1f367fdee8a9e6ceb3211348091b
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