Instructions to use ProbeX/Model-J__ResNet__model_idx_0850 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_0850 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_0850") 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_0850") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0850", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1f29576fa2dd9c29748d79221d1196c55175cb12e38c4a9ce33fc982a648615e
- Size of remote file:
- 171 MB
- SHA256:
- fb56f56605c033a02aac90c95f72cdeffd2b56230aa6e10e9125f8d66e950b4d
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