Instructions to use ProbeX/Model-J__ResNet__model_idx_0486 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_0486 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_0486") 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_0486") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0486") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 42f37461b234d33efe29fa69637528be22e429f903f96a947ea49b9220523959
- Size of remote file:
- 5.37 kB
- SHA256:
- 19d7bea4daaa4f42ebbe3764a3f0efd095a462d601c4b4cc9387733e2fd72fcd
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