Instructions to use ProbeX/Model-J__ResNet__model_idx_0993 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_0993 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_0993") 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_0993") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0993", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- ca9fc2265b04a01e11d560675d8fdd599d1dceac9ebf9c5fd34dabc3cc9fb820
- Size of remote file:
- 171 MB
- SHA256:
- 781ca27f251c5bef4ac566728aceda5529382c8c6d421cc57357855be2bf565c
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