Instructions to use ProbeX/Model-J__ResNet__model_idx_0326 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_0326 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_0326") 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_0326") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0326") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 59d59b7ba11778ae80c502e2485d95604f5219b536274061542ea107ef88ea86
- Size of remote file:
- 5.37 kB
- SHA256:
- ca02a7551a23cba71af6fe3b049607c06cdc7d1ba4c1919c0032c34c913588eb
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