Instructions to use ProbeX/Model-J__ResNet__model_idx_0025 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_0025 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_0025") 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_0025") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0025") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6faa241b28f405e1fee70cd583e089ca61bf62a256dce9fae09fa7ab00339336
- Size of remote file:
- 5.37 kB
- SHA256:
- 14906a7db24a8df6adf78e0ae58307a3cea2d951e2633e5edfe5718d3ddf2f39
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