fairface age image detection

Providerdima806
Categoryimage-classification
Licenseapache-2.0
Downloads69
Stars0

Overview

The FairFace age detection model is a specialized image classification tool designed to mitigate demographic bias in age estimation. Unlike generic vision models, FairFace is trained on a balanced dataset across multiple ethnic groups, making it a reliable choice for developers building inclusive applications. It integrates easily into standard computer vision pipelines for automated user profiling, KYC verification, or targeted content delivery. For developers, this model offers a lightweight alternative to heavy multi-task transformers when the primary requirement is objective, fair age categorization across diverse global populations.

Highlights

  • Reduced demographic bias for fair global age estimation
  • Optimized for image classification and user profiling
  • Apache-2.0 license ensures flexible commercial integration
  • High reliability across diverse ethnic datasets

Usage

Install
# Install Hugging Face transformers
pip install transformers torch
SDK Usage
# Load model with transformers
from transformers import AutoModel, AutoTokenizer

model = AutoModel.from_pretrained("dima806/fairface_age_image_detection")
tokenizer = AutoTokenizer.from_pretrained("dima806/fairface_age_image_detection")

Hugging Face Download

We recommend downloading the model via the Hugging Face CLI or Hub SDK.

Guidance：Before downloading, install huggingface_hub with:

Guidance 
pip install -U huggingface_hub

CLI Download

Download the full repository

Download the full repository 
huggingface-cli download dima806/fairface_age_image_detection

Download a single file to a local folder (e.g. config.json into ./dir)

Download a single file to a local folder (e.g. config.json into ./dir) 
huggingface-cli download dima806/fairface_age_image_detection config.json --local-dir ./dir

See the official docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from huggingface_hub import snapshot_download
model_dir = snapshot_download('dima806/fairface_age_image_detection')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://huggingface.co/dima806/fairface_age_image_detection

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://huggingface.co/dima806/fairface_age_image_detection

Model files are hosted on the Hugging Face Hub — download directly via HF CLI / SDK / Git, not through this site.

PyTorch / Transformers Usage

Install Transformers

Install Transformers 
pip install -U transformers torch

Load the model and run inference

Load the model and run inference 
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained('dima806/fairface_age_image_detection')
tokenizer = AutoTokenizer.from_pretrained('dima806/fairface_age_image_detection')

Model Download

We recommend downloading the model via the ModelScope CLI or SDK.

Guidance：Before downloading, install ModelScope with:

Guidance 
pip install modelscope

CLI Download

Download the full repository

Download the full repository 
modelscope download --model dima806/fairface_age_image_detection

Download a single file to a local folder (e.g. README.md into ./dir)

Download a single file to a local folder (e.g. README.md into ./dir) 
modelscope download --model dima806/fairface_age_image_detection README.md --local_dir ./dir

See the docs for more CLI options

SDK Download

SDK Download 
# 模型下载
from modelscope import snapshot_download
model_dir = snapshot_download('dima806/fairface_age_image_detection')

Git Download

Make sure git-lfs is installed first

Git Download 
git lfs install
git clone https://www.modelscope.cn/dima806/fairface_age_image_detection.git

To skip LFS large-file downloads, use:

Skip LFS 
GIT_LFS_SKIP_SMUDGE=1 git clone https://www.modelscope.cn/dima806/fairface_age_image_detection.git

ModelScope 模型页直接下载模型文件；无需将模型文件放在本站服务器。

Notebook Quickstart

Install the ModelScope library

Install the ModelScope library 
pip install "modelscope[audio,cv,nlp,multi-modal,science]" -f https://modelscope.oss-cn-beijing.aliyuncs.com/releases/repo.html

Load the model and run inference

Load the model and run inference 
from modelscope.pipelines import pipeline
from modelscope.utils.constant import Tasks

p = pipeline('text-generation', 'dima806/fairface_age_image_detection')

Full Documentation

来源: HuggingFace

---
license: apache-2.0
metrics:

  • accuracy

  • f1

base_model:
  • google/vit-base-patch16-224-in21k

pipeline_tag: image-classification
library_name: transformers
datasets:
  • nateraw/fairface

---
Detects age group with about 59% accuracy based on an image.

See https://www.kaggle.com/code/dima806/age-group-image-classification-vit for details.

!image/png

code
Classification report:

precision    recall  f1-score   support


0-2     0.7803    0.7500    0.7649       180
         3-9     0.7998    0.7998    0.7998      1249
       10-19     0.5361    0.4236    0.4733      1086
       20-29     0.6402    0.7221    0.6787      3026
       30-39     0.4935    0.5083    0.5008      2099
       40-49     0.4848    0.4386    0.4606      1238
       50-59     0.5000    0.4814    0.4905       725
       60-69     0.4497    0.4685    0.4589       286
more than 70     0.6897    0.1802    0.2857       111


accuracy                         0.5892     10000
   macro avg     0.5971    0.5303    0.5459     10000
weighted avg     0.5863    0.5892    0.5844     10000

View official source
Join our Telegram