Instructions to use ProbeX/Model-J__ResNet__model_idx_0013 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_0013 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_0013") 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_0013") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0013") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 34c3a5c2cd99eb40445f5483ed741a3ca058c81b67630de1ab3323d136b7ee3a
- Size of remote file:
- 171 MB
- SHA256:
- 85702766db553f76feda7c739230545dd1a776bb632747fd8930d92cdd338a72
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