Instructions to use ProbeX/Model-J__ResNet__model_idx_1000 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_1000 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_1000") 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_1000") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_1000") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 92477c0fbed1310cba6e98341a745d77ca4a83cb4ebbd6f1706e33136d932186
- Size of remote file:
- 5.37 kB
- SHA256:
- c5141b49bb107ccde931626d5a6c82abc54d9ff9db257f867857522d4eddec11
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