Instructions to use ProbeX/Model-J__ResNet__model_idx_0529 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_0529 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_0529") 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_0529") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0529") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 06fc294452e7ac0b7f7eddcbed24be1a285fb00d214e383003e47d0339767cbc
- Size of remote file:
- 5.37 kB
- SHA256:
- c5733ed91c91dbe6258bf85ed9f6963e028508eaf6886182a7fc24f8f1b59efd
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