Instructions to use ProbeX/Model-J__ResNet__model_idx_0066 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_0066 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_0066") 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_0066") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0066") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 71718c69acc3bcc60ba0cc0b73b70c092bd2c5d362515e72f99e01971f878cc9
- Size of remote file:
- 5.37 kB
- SHA256:
- 02571a3fb00f32a32f2eb3c7897eec712e48a562b46451568e2f2ec88f4aa3b4
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