Instructions to use ProbeX/Model-J__ResNet__model_idx_0207 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_0207 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_0207") 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_0207") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0207") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 58b9d881ed492fc2263b16e0c29deb8cb333811e0c1fb9df77e3a3740993222f
- Size of remote file:
- 5.37 kB
- SHA256:
- 95b7a6589a1bc3918164979216492e178b6bdd9f781e54335d54056e7e74bb04
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