Instructions to use ProbeX/Model-J__ResNet__model_idx_0928 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_0928 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_0928") 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_0928") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0928", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4c2d7f21bc2e9f48899aa6dc11db0e8f6f5816edd6d1eda75c8102d121692365
- Size of remote file:
- 5.37 kB
- SHA256:
- 622a9b0ec3041e1bc9d9f61405e2cf6a843ab6b82b7c6c9039b3603c6fb5a302
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