Instructions to use ProbeX/Model-J__ResNet__model_idx_0018 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_0018 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_0018") 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_0018") model = AutoModelForImageClassification.from_pretrained("ProbeX/Model-J__ResNet__model_idx_0018", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 4e4bd5a7074b5149cde179a80c0c84b296d8510f90d6623570b4ded23c395c49
- Size of remote file:
- 5.37 kB
- SHA256:
- 66f8ba8815dcc2efede0e468b5654d4e4e88425e0a27568678500b560e79f592
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