Instructions to use ProbeX/Model-J__ResNet__model_idx_0779 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_0779 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_0779") 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_0779") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0779", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- efd5c34b15619cf3ae79b25332d821aa3d5f8b308e1bcaa6cabe1cf6212e0708
- Size of remote file:
- 171 MB
- SHA256:
- 59fba1c7ea744a9982814f5217016f1e9564de36ecbccfd31242e76f7861c17b
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