Instructions to use rchan26/dit_base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use rchan26/dit_base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-classification", model="rchan26/dit_base") 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("rchan26/dit_base") model = AutoModelForImageClassification.from_pretrained("rchan26/dit_base") - Notebooks
- Google Colab
- Kaggle
Ryan CHan commited on
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# dit_base
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This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the davanstrien/
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It achieves the following results on the evaluation set:
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- Loss: 0.4527
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- Accuracy: 0.8190
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# dit_base
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This model is a fine-tuned version of [microsoft/dit-base](https://huggingface.co/microsoft/dit-base) on the davanstrien/leicester_loaded_annotations dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4527
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- Accuracy: 0.8190
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