Instructions to use ProbeX/Model-J__ResNet__model_idx_0796 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_0796 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_0796") 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_0796") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0796") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1cc96a275ac9b6d3c3c434866cf2a6226fb85e49f8aa1091412ea79dde9f14de
- Size of remote file:
- 171 MB
- SHA256:
- cf7d3fb2feb4380c30b693ffe62f3df388f08950fb07d3d4ae256c9a2bc3a4e3
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