Instructions to use ProbeX/Model-J__ResNet__model_idx_0413 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_0413 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_0413") 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_0413") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0413", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 065e572f632b6194486d72ee064b0e4e11c755f2ff4f65c36c874d7c550f455d
- Size of remote file:
- 5.37 kB
- SHA256:
- 9f14e25baf429ad962e63a37004a881ac6422a16cf564433903b68fb5158475f
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