Instructions to use ProbeX/Model-J__ResNet__model_idx_0969 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_0969 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_0969") 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_0969") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0969", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 1c2a6e281726e75e74e2613e7f521c8a2e22a4b2b9e5d1ed9270ba00ab38b583
- Size of remote file:
- 171 MB
- SHA256:
- c77b87a72b91dfc2dc8e30ede18b98a3df126399736af2d4112f1d35d83ef370
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