Instructions to use ProbeX/Model-J__ResNet__model_idx_0790 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_0790 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_0790") 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_0790") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0790", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34cf6add99d06fcbf06f8bdac01e2c7bd29a775cc16e6ba0a14a613dcd979663
- Size of remote file:
- 5.37 kB
- SHA256:
- 4bc97f6154ddac6c287ab0da1e3f58a5fc987830ddef1ff2f1780a2f370bb202
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