Instructions to use ProbeX/Model-J__ResNet__model_idx_0283 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_0283 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_0283") 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_0283") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0283") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ade8596c44b7465d4d57aa2196908f12d3963ea401c6605dce773dae04934ffe
- Size of remote file:
- 5.37 kB
- SHA256:
- ff8a032e7e7113e762d5203595ee89a37de5d555af97154bc885315237614cb6
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