Instructions to use ProbeX/Model-J__ResNet__model_idx_0409 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_0409 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_0409") 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_0409") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0409", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7ab9414088e0ec1e28d608624d47960fa17f74123e77240d5d1e0bb524caa310
- Size of remote file:
- 171 MB
- SHA256:
- 4b6b713fbc7644585fe14326125aa37735cdeaaed5fa8c6ed8bc0d9bdf74332a
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