Instructions to use ProbeX/Model-J__ResNet__model_idx_0951 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_0951 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_0951") 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_0951") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0951") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 93408847c5554232933321c69d56f990d06e89995bdd1223cd2203c29656563e
- Size of remote file:
- 5.37 kB
- SHA256:
- 65b9a9e768a60604c44e37628fb43ebe100fd4598832d7260bc42cc9febf080d
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