Instructions to use fassabilf/results with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use fassabilf/results with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="fassabilf/results") 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("fassabilf/results") model = AutoModelForImageClassification.from_pretrained("fassabilf/results", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8b50188cd940f28d02337c6087815fcd02429bef2e499305289b90893d7c6596
- Size of remote file:
- 5.24 kB
- SHA256:
- b98390448ca83db8f8bde1136e656b5456cd6634aadb346c5fb2953e83ca09d9
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