Instructions to use ProbeX/Model-J__ResNet__model_idx_0789 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_0789 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_0789") 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_0789") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0789", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 14ac9813b0fef74b3f9c3fc03d168a26f71f20813bfdcc6b4e8cf1eb661440a8
- Size of remote file:
- 171 MB
- SHA256:
- 926d21a079b4b638e1b4b27b6dc292dbfec287fd3d3204a25fa7f8509952c305
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