Instructions to use ProbeX/Model-J__ResNet__model_idx_0170 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_0170 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_0170") 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_0170") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0170", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7951d597409dea2bf0b48dce82a26d56a6fa4232f30c2ffa1f1899f9842e2e54
- Size of remote file:
- 171 MB
- SHA256:
- bb9beb5baab9cfc81b8d1571cfd079fd39578aa5efd2c98c0d8c1cad15c9bf0a
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