Instructions to use ProbeX/Model-J__ResNet__model_idx_0513 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_0513 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_0513") 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_0513") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0513") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- cfc7964442c00bbedb5b39b43bf3832dc92f22ca9c31bfbfe639a95c3143e9a9
- Size of remote file:
- 171 MB
- SHA256:
- 5d22a95c4d4607d70a29c78b2fa60957a1829271f79d9a946b1d9e296d1e697e
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