Instructions to use ProbeX/Model-J__ResNet__model_idx_0964 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_0964 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_0964") 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_0964") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0964", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 169761cf362b7693fb982fac2cf92003c3bc35b42c526c80962fa414b81e5738
- Size of remote file:
- 171 MB
- SHA256:
- 0d500b15c811fb2b83f2f822604a7dc71af1ff58457d11c3a96b6ed21dfdc544
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