Instructions to use ProbeX/Model-J__ResNet__model_idx_0970 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_0970 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_0970") 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_0970") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0970", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02d622f172f8209cb407a133022fb6359cf9689252cd7a2dcc29a9269d83138a
- Size of remote file:
- 5.37 kB
- SHA256:
- 4a66a468b216a577bce456655afb7fbf1381b2b765431598b18464e25b025ba4
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