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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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- YOLO11 |
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base_model: |
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- Ultralytics/YOLOv8 |
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- Ultralytics/YOLO11 |
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library_name: ultralytics |
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pipeline_tag: image-classification |
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model-index: |
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- name: v8n |
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results: |
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- task: |
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type: Image Classification |
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dataset: |
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name: FairFace |
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type: FairFace |
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metrics: |
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- name: top1_acc |
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type: top1_acc |
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value: 0.717 |
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- name: v8s |
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results: |
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- task: |
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type: Image Classification |
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dataset: |
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name: FairFace |
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type: FairFace |
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metrics: |
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- name: top1_acc |
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type: top1_acc |
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value: 0.721 |
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- name: v8m |
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results: |
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- task: |
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type: Image Classification |
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dataset: |
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name: FairFace |
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type: FairFace |
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metrics: |
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- name: top1_acc |
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type: top1_acc |
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value: 0.725 |
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- name: 11l |
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results: |
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- task: |
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type: Image Classification |
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dataset: |
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name: FairFace |
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type: FairFace |
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metrics: |
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- name: top1_acc |
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type: top1_acc |
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value: 0.733 |
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- name: 11x |
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results: |
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- task: |
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type: Image Classification |
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dataset: |
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name: FairFace |
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type: FairFace |
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metrics: |
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- name: top1_acc |
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type: top1_acc |
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value: 0.735 |
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--- |
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# Race Classification YOLOv8/11 |
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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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I've made simple scripts for you to use on your data. By default it will output .txt files(or append to existing), so modify for your specific needs: |
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https://github.com/Anzhc/Simple-Utility-Scripts-for-YOLO/tree/main |
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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 | 0.721 | 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 | 0.725 | 7(Black, East Asian, Indian, Latino_Hispanic, Middle Eastern, Southeast Asian, White) |~86740(train), ~10950(val)|224| |
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|Race-CLS-FairFace_yolo11l| Face: Real | 0.733 | 7(Black, East Asian, Indian, Latino_Hispanic, Middle Eastern, Southeast Asian, White) |~86740(train), ~10950(val)|224| |
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|Race-CLS-FairFace_yolo11x| Face: Real | 0.735 | 7(Black, East Asian, Indian, Latino_Hispanic, Middle Eastern, Southeast Asian, White) |~86740(train), ~10950(val)|224| |
