Instructions to use ProbeX/Model-J__ResNet__model_idx_0955 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_0955 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_0955") 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_0955") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0955", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 5cc4d8c7bced08ad02b066a90f22bd411e13a1b629aa2edbd9bec35cf78de99e
- Size of remote file:
- 171 MB
- SHA256:
- 0ff05e60f8bda7101618f0d63b01a11399fe5d1aa67fa45b9a5cc16b25b6578f
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