Instructions to use ProbeX/Model-J__ResNet__model_idx_0064 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_0064 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_0064") 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_0064") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0064", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 810c828b15de96a3ec6ac39c5e85cd56f1dcf7b6196ee8cf9cf4ff56dea9b4d2
- Size of remote file:
- 5.37 kB
- SHA256:
- 4630cfd7756b326f96fbc1349f4b9dcb1b35b07fdaa6af63a098c1ee7fed3f3b
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