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---
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| |