Instructions to use mthandazo/output-models with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mthandazo/output-models with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="mthandazo/output-models") 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("mthandazo/output-models") model = AutoModelForImageClassification.from_pretrained("mthandazo/output-models") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8f776f91c71d13fa2477332b438a136bb28f85e15ed3503076c9e68bf2443c7a
- Size of remote file:
- 5.05 kB
- SHA256:
- 4da012da8dee11673b48d10aab0c4b8af20b709168d49eda053fe6dacdcb4282
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