Instructions to use ProbeX/Model-J__ResNet__model_idx_0219 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_0219 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_0219") 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_0219") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0219") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6cffd6bc5e6a75523d47a7908428ab94f3f4cfb1a13a78d78b9de00465cd954f
- Size of remote file:
- 5.37 kB
- SHA256:
- 2e96a4c64bcd3213a9836fddfb6ff16e731c0192f7b6f0e63c9cc43b86f5f0a4
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