Instructions to use ProbeX/Model-J__ResNet__model_idx_0239 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_0239 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_0239") 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_0239") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0239", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 218a507bd383566b38554af8c8991b55c7f212b59753f3d8f33d91946253b75f
- Size of remote file:
- 171 MB
- SHA256:
- d97267851db67c0427c4e67cb2e6f21d83c7e4bd4fd788fec6dbfe9507acaf13
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