Instructions to use ProbeX/Model-J__ResNet__model_idx_0341 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_0341 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_0341") 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_0341") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0341", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2c12518af975b5c7fb91cf0a37ef396354b4370fc496a33d0afd1bad3d7431f9
- Size of remote file:
- 5.37 kB
- SHA256:
- 5e409855d59b4f19635d8b7af7b2d6d8f9416cf43e8bc18c8dcf447385dfa8cb
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