Instructions to use ProbeX/Model-J__ResNet__model_idx_0927 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_0927 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_0927") 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_0927") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0927", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eeeff490733325870222571a5bcae1697b27e64fafbfa4f73079b3439983d147
- Size of remote file:
- 171 MB
- SHA256:
- a2d950f2a9b10a360b26a95d1ad0be0a8ba3af63f490909c601b495a56d2541c
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