Instructions to use ProbeX/Model-J__ResNet__model_idx_0744 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_0744 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_0744") 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_0744") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0744", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- eb4f14d96f334db309e944764e013c07ef36550543a3e5243cf7d881c21c7239
- Size of remote file:
- 171 MB
- SHA256:
- 440023198b008cd551027686aeb9654e92f478012db8f1453136fcef5f62e01f
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