Instructions to use ProbeX/Model-J__ResNet__model_idx_0184 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_0184 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_0184") 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_0184") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0184") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8d46c5e0891f7a0b64bef139805c3b49a00ba1933ac338952591ffb825133d82
- Size of remote file:
- 5.37 kB
- SHA256:
- f9cf5a23e9afece2200415b96350bdfa26d48b793f9c0385a45a412919513d8a
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