Instructions to use ProbeX/Model-J__ResNet__model_idx_0763 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_0763 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_0763") 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_0763") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0763", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- f8f1edba20c0f5c4587c7ad1f628f0f94537dbbe5904efac1a96e42faef34d4f
- Size of remote file:
- 171 MB
- SHA256:
- 610f2ca6c95ae1550819c9928d982d33f1864108b49d64a2a945396094c6b915
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