Instructions to use ProbeX/Model-J__ResNet__model_idx_0809 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_0809 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_0809") 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_0809") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0809") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6ebe52d967895c6e1f3b9e7dfb7b1fa91a821aed56231473c979562d37baa04c
- Size of remote file:
- 171 MB
- SHA256:
- 805c99d618f4deac65cf35fe970ef6f3ba27836ccb2e478a52fec529466a5b99
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