Instructions to use ProbeX/Model-J__ResNet__model_idx_0650 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_0650 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_0650") 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_0650") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0650", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1727087cc5fbecfe1f5b85562511e1b7339171d3f1d73aec5a41dd00675be9fd
- Size of remote file:
- 171 MB
- SHA256:
- cf452bca74ddfbd195f593aa976eb2c0e01525df1d2b018cacfb27e9beaf9b81
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