---
license: agpl-3.0
tags:
- pytorch
- YOLOv8
- Ultralytics
- YOLO11
base_model:
- Ultralytics/YOLOv8
- Ultralytics/YOLO11
library_name: ultralytics
pipeline_tag: image-classification
model-index:
  - name: v8n
    results:
      - task:
          type: Image Classification
        dataset:
          name: FairFace
          type: FairFace
        metrics:
          - name: top1_acc
            type: top1_acc
            value: 0.717
  - name: v8s
    results:
      - task:
          type: Image Classification
        dataset:
          name: FairFace
          type: FairFace
        metrics:
          - name: top1_acc
            type: top1_acc
            value: 0.721
  - name: v8m
    results:
      - task:
          type: Image Classification
        dataset:
          name: FairFace
          type: FairFace
        metrics:
          - name: top1_acc
            type: top1_acc
            value: 0.725
  - name: 11l
    results:
      - task:
          type: Image Classification
        dataset:
          name: FairFace
          type: FairFace
        metrics:
          - name: top1_acc
            type: top1_acc
            value: 0.733
  - name: 11x
    results:
      - task:
          type: Image Classification
        dataset:
          name: FairFace
          type: FairFace
        metrics:
          - name: top1_acc
            type: top1_acc
            value: 0.735
---
# Race Classification YOLOv8/11
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.
> **Karkkainen, Kimmo, and Joo, Jungseock.**  
> *FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation.*  
> Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2021, pp. 1548–1558.  
> [FairFace Dataset on GitHub](https://github.com/joojs/fairface)

You also can find their pretrained model [here](https://github.com/dchen236/FairFace).

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!
~~I will provide a pipeline for running it on your datasets in future.~~

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:
https://github.com/Anzhc/Simple-Utility-Scripts-for-YOLO/tree/main


| Model                       | Target     | top1_acc      |Classes        |Dataset size  |Training Resolution|
| --------------------------- | ---------- | ------------- | ------------- |---------------|-------------------|
  |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|
  |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|
  |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|
  |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|
  |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|