Instructions to use ProbeX/Model-J__ResNet__model_idx_0425 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_0425 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_0425") 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_0425") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0425") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 406e0a3a20703c7d40cc6060603c4be62b6d2f22c351864123b35c3ab6047825
- Size of remote file:
- 5.37 kB
- SHA256:
- 02e1c6fff3e10834fba7dc3890a7b027a9fb20a6b44c4301031d142ae257b510
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