Instructions to use ProbeX/Model-J__ResNet__model_idx_0175 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_0175 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_0175") 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_0175") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0175") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7cb34cad4429304a1be161a13a52a550a40e493502a4aa599f0e3cd7ce5a2af7
- Size of remote file:
- 5.37 kB
- SHA256:
- 2d8a473c9bf15a059035a80ca7ca8901d02d5565bd01222150c57648ae15a7b4
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