Instructions to use ProbeX/Model-J__ResNet__model_idx_0074 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_0074 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_0074") 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_0074") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0074", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 90ceb34f8a39a0e336c513f1014c058942cc3d44009c7b80abc4f2ada140bad6
- Size of remote file:
- 5.37 kB
- SHA256:
- 50ed1c17620b113cf92bf1a5d2a7f30cca1825317192e5a6289f1180309a82c8
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