Instructions to use ProbeX/Model-J__ResNet__model_idx_0709 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_0709 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_0709") 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_0709") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0709") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cd80b5cf75cea2be8dc17e0ae89cb9404b54dee6ad54eec8ed2b936ad4dc911e
- Size of remote file:
- 5.37 kB
- SHA256:
- aec8b8711e5e10a5f3f8898cda837b40fbb146506560124ea8b3d8c3c6d02bb0
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