Instructions to use ProbeX/Model-J__ResNet__model_idx_0952 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_0952 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_0952") 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_0952") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0952") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 66cfe0158f62267b28047a678c1adf53270b85f04056cd0ea916752cd52f3335
- Size of remote file:
- 5.37 kB
- SHA256:
- 6ab8749da74b17bf2498c749017f510c1832511a7c1d12389900a0913e54eb06
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