Instructions to use ProbeX/Model-J__ResNet__model_idx_0866 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_0866 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_0866") 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_0866") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0866", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 9ef99051480d82db5c55f87771f0ccca42b5a30ed32b2ff029a883f80c51982f
- Size of remote file:
- 5.37 kB
- SHA256:
- a4b098bcecc948981cb4d7e12380e361a90a6e87d45c6dd767c46ffc33dfe1cc
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