Instructions to use ProbeX/Model-J__ResNet__model_idx_0437 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_0437 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_0437") 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_0437") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0437") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 324317a1c58fb8766594dd2de211a3ccd42d869cc1a6d6081164524714d95011
- Size of remote file:
- 5.37 kB
- SHA256:
- 97bfcdb82ca2a3cfd52bac3e518453b133f882095e7b92b8c41cb7f01f099873
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