language:
- en
license: cc-by-4.0
library_name: pytorch
pipeline_tag: image-classification
tags:
- image-classification
- fairface
- face-attribute
- pytorch
- timm
datasets:
- HuggingFaceM4/FairFace
base_model: timm/eva02_large_patch14_224.mim_in22k
metrics:
- accuracy
- f1
FairFace-MonkeyCake-3Task-224
Overview
A multi-task face attribute classification model based on EVA02-L/14 (MIM-22K) (timm/eva02_large_patch14_224.mim_in22k), trained on the HuggingFaceM4/FairFace dataset. Predicts three demographic attributes simultaneously: race, gender, and age.
Teacher model with highest overall accuracy; 303M parameters and 344 img/s. Best suited for offline benchmarking rather than real-time edge deployment.
Model Details
| Field | Value |
|---|---|
| Backbone | EVA02-L/14 (MIM-22K) (timm/eva02_large_patch14_224.mim_in22k) |
| Parameters | 303,286,225 |
| Input Resolution | RGB, 224x224 |
| Normalization | mean=(0.4815, 0.4578, 0.4082), std=(0.2686, 0.2613, 0.2758) |
| Output Targets | race (7 classes) + gender (2 classes) + age (8 ordinal thresholds / 9 brackets) |
| Test Set | 10,931 images (frozen deduplicated FairFace official validation set) |
Training Data
Trained on the HuggingFaceM4/FairFace dataset (Karkkainen & Joo, WACV 2021; 97,698 raw images).
Deduplicated by content (SHA-256) for this project:
- Train: 77,926 images
- Selection (val): 8,659 images
- Test: 10,931 images
All pairwise set intersections are zero; 0 images from the official training partition entered the evaluation set.
Evaluation Results
| Task | Accuracy | Macro-F1 | Support |
|---|---|---|---|
| Race (7 classes) | 77.02% | 76.83% | 10,931 |
| Gender (2 classes) | 96.87% | 96.86% | 10,931 |
| Age (9 ordinal brackets) | 63.93% | 61.29% | 10,931 |
3-Task Average Accuracy: 79.27%. Age MAE: 0.3838 brackets, +/-1 bracket accuracy: 97.94%.
Per-Class Performance
Usage
import inference
model = inference.load("model.pt")
predictions = inference.predict(model, "face.jpg")
print(predictions)
Dependencies: torch, timm, torchvision, pillow.
Limitations & Ethical Considerations
- Research Use Only: This model is developed strictly for face attribute research, algorithmic bias measurement, and fairness auditing. It must NOT be used for surveillance, identity verification, recruitment, credit evaluation, or any high-risk automated decision-making.
- Evaluation Set Reuse: This frozen test set was evaluated across iterative checkpoints during development; reported metrics are descriptive.
- Single Random Seed: Training was conducted under a single seed; performance deltas under 0.3 pp should be considered statistical noise.
- Demographic Disparities: Demographic boundaries (notably between Latino/Hispanic, Middle Eastern, Indian, and White groups) are non-discrete; age estimation exhibits higher error rates, and specific intersectional gender x race subgroups show higher disparities.
- Label Attribution: Labels reflect the categorical annotations of the FairFace dataset and do not represent individual personal self-identification or genuine heritage.
Data & Licensing
- Dataset: HuggingFaceM4/FairFace, CC BY 4.0 (images sourced from YFCC100M under respective licenses).
- Model Checkpoint: Derivative work trained on FairFace, released under CC BY 4.0.
- Pretrained Backbone: timm/eva02_large_patch14_224.mim_in22k (Apache-2.0).
Citation
@inproceedings{karkkainen2021fairface,
title={FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age for Bias Measurement and Mitigation},
author={Karkkainen, Kimmo and Joo, Jungseock},
booktitle={WACV},
year={2021}
}