Instructions to use ProbeX/Model-J__ResNet__model_idx_0571 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_0571 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_0571") 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_0571") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0571", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- a3a836a3dd8b7b353638610d9267d8030ae682ac5deb270ac006aba7440b5fb6
- Size of remote file:
- 171 MB
- SHA256:
- f22040678ed450a537af5f6989d0e25df297d6bd0917507490fa9ed351369c45
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