Instructions to use ProbeX/Model-J__ResNet__model_idx_0364 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_0364 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_0364") 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_0364") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0364") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8af7af804dc7b74c6a9355aed0ead069c8164945c657a8a50a179731ae82d7ac
- Size of remote file:
- 5.37 kB
- SHA256:
- 8de81c3e6733a277fc55afcc8a49760d771f7c28a7f9f174fe3fd3c84deb1c2e
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