Instructions to use ProbeX/Model-J__ResNet__model_idx_0985 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_0985 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_0985") 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_0985") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0985") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7637c278cf5e7e9de9de08e0c2f9d637fa1937338e68ba16a6636a534006ad7f
- Size of remote file:
- 5.37 kB
- SHA256:
- b8eb8815677198c9e49991f3d2be8cefa3f8d5d2b9bce4a5e239a10912f4599a
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