Instructions to use ProbeX/Model-J__ResNet__model_idx_0818 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_0818 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_0818") 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_0818") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0818", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 41d2a5f0ec1fd4cd1834c6a15aabcd64aa875c28a7ba7e90f55473799cd26ccf
- Size of remote file:
- 171 MB
- SHA256:
- 3d482446d9d666ef28e28c004541a226a8063090aef164d28c7696c62cc71066
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