Instructions to use ProbeX/Model-J__ResNet__model_idx_0146 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_0146 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_0146") 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_0146") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0146", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 21eefa57ca13ccea730710d0e44737dfbd6e926bf1760a003d8dd643e135dab7
- Size of remote file:
- 171 MB
- SHA256:
- a5c64d4159709ef4de8bb4f8b8f8dc22565bc2227b3ea06a641dc3ab2228151c
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