Instructions to use kmsr75/outputs with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kmsr75/outputs with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="kmsr75/outputs") 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("kmsr75/outputs") model = AutoModelForImageClassification.from_pretrained("kmsr75/outputs", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- da4d73adb8d63aa49a1a6968b248c79c3da35769d06c68558bf69c363e38af11
- Size of remote file:
- 4.28 kB
- SHA256:
- 43e97549ae4367ab7a6c1947b81f85e49a84d6f5e0314181bb72b3f347f86d43
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