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license: agpl-3.0
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---
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license: agpl-3.0
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tags:
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- pytorch
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- YOLOv8
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- Ultralytics
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base_model:
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- Ultralytics/YOLOv8
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library_name: ultralytics
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pipeline_tag: object-detection
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---
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# Race Classification YOLOv8
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This model is based on [FairFace](https://github.com/joojs/fairface) 0.25 padding variant dataset composed by Microsoft researchers, aiming to reduce bias by better balancing classes in dataset.
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> **Karkkainen, Kimmo, and Joo, Jungseock.**
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> *FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation.*
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> Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021, pp. 1548–1558.
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> [FairFace Dataset on GitHub](https://github.com/joojs/fairface)
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You also can find their pretrained model [here](https://github.com/dchen236/FairFace).
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This YOLOv8 training is meant only for race classification. I wanted a really really fast model for tagging, and this is likely what it's useful for!
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I will provide a pipeline for running it on your datasets in future.
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| Model | Target | top1_acc |Classes |Dataset size |Training Resolution|
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| --------------------------- | ---------- | ------------- | ------------- |---------------|-------------------|
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|Race-CLS-FairFace_yolov8n| Face: Real | 0.717 | 7(Black, East Asian, Indian, Latino_Hispanic, Middle Eastern, Southeast Asian, White) |~86740(train), ~10950(val)|224|
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|Race-CLS-FairFace_yolov8s| Face: Real | IN PROGRESS | 7(Black, East Asian, Indian, Latino_Hispanic, Middle Eastern, Southeast Asian, White) |~86740(train), ~10950(val)|224|
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|Race-CLS-FairFace_yolov8m| Face: Real | IN PROGRESS | 7(Black, East Asian, Indian, Latino_Hispanic, Middle Eastern, Southeast Asian, White) |~86740(train), ~10950(val)|224|
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