Instructions to use ProbeX/Model-J__ResNet__model_idx_0894 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_0894 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_0894") 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_0894") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0894") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 13cd1b94441df2aa11ba79a34fd68574a66122dc8c8ad58d91b979da3f01b22e
- Size of remote file:
- 5.37 kB
- SHA256:
- dd91ca6db9aa0f80b1aff42093524aa0c034f8553c8d2cc814d2756612dd28af
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