Instructions to use ProbeX/Model-J__ResNet__model_idx_0634 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_0634 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_0634") 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_0634") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0634") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 670646da6f7b71354bc40ab67032821b5953b58e21b22aebff4fb78ee604cb57
- Size of remote file:
- 5.37 kB
- SHA256:
- 9777cbc291dcd25e04fa789580f28f5922b3cd4cf37cfe80a66b981d7b3cb918
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