Instructions to use ProbeX/Model-J__ResNet__model_idx_0848 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_0848 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_0848") 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_0848") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0848") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1542df96e3abfb31bb3bf8c2de9b8339967877150b9ef45003f454a5cf695205
- Size of remote file:
- 5.37 kB
- SHA256:
- 82566cf1f14a2acb9811d56403490488aeb9d6de970314b4658dbd0b551076f3
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