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<div align="center">
<p>
<a align="center" target="_blank">
<img width="900" src="./images/MiVOLO.jpg"></a>
</p>
<br>
</div>
## MiVOLO: Multi-input Transformer for Age and Gender Estimation
[](https://paperswithcode.com/sota/age-estimation-on-utkface?p=mivolo-multi-input-transformer-for-age-and) [](https://paperswithcode.com/sota/age-estimation-on-imdb-clean?p=beyond-specialization-assessing-the-1) [](https://paperswithcode.com/sota/facial-attribute-classification-on-fairface?p=beyond-specialization-assessing-the-1) [](https://paperswithcode.com/sota/age-and-gender-classification-on-adience?p=beyond-specialization-assessing-the-1) [](https://paperswithcode.com/sota/age-and-gender-classification-on-adience-age?p=beyond-specialization-assessing-the-1) [](https://paperswithcode.com/sota/age-and-gender-estimation-on-lagenda-age?p=beyond-specialization-assessing-the-1) [](https://paperswithcode.com/sota/gender-prediction-on-lagenda?p=beyond-specialization-assessing-the-1) [](https://paperswithcode.com/sota/age-estimation-on-agedb?p=mivolo-multi-input-transformer-for-age-and) [](https://paperswithcode.com/sota/gender-prediction-on-agedb?p=mivolo-multi-input-transformer-for-age-and) [](https://paperswithcode.com/sota/age-estimation-on-cacd?p=beyond-specialization-assessing-the-1)
> [**MiVOLO: Multi-input Transformer for Age and Gender Estimation**](https://arxiv.org/abs/2307.04616),
> Maksim Kuprashevich, Irina Tolstykh,
> *2023 [arXiv 2307.04616](https://arxiv.org/abs/2307.04616)*
> [**Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation**](https://arxiv.org/abs/2403.02302),
> Maksim Kuprashevich, Grigorii Alekseenko, Irina Tolstykh
> *2024 [arXiv 2403.02302](https://arxiv.org/abs/2403.02302)*
[[`Paper 2023`](https://arxiv.org/abs/2307.04616)] [[`Paper 2024`](https://arxiv.org/abs/2403.02302)] [[`Demo`](https://huggingface.co/spaces/iitolstykh/age_gender_estimation_demo)] [[`Telegram Bot`](https://t.me/AnyAgeBot)] [[`BibTex`](#citing)] [[`Data`](https://wildchlamydia.github.io/lagenda/)]
## MiVOLO pretrained models
Gender & Age recognition performance.
<table style="margin: auto">
<tr>
<th align="left">Model</th>
<th align="left" style="color:LightBlue">Type</th>
<th align="left">Dataset (train and test)</th>
<th align="left">Age MAE</th>
<th align="left">Age CS@5</th>
<th align="left">Gender Accuracy</th>
<th align="left">download</th>
</tr>
<tr>
<td>volo_d1</td>
<td align="left">face_only, age</td>
<td align="left">IMDB-cleaned</td>
<td align="left">4.29</td>
<td align="left">67.71</td>
<td align="left">-</td>
<td><a href="https://drive.google.com/file/d/17ysOqgG3FUyEuxrV3Uh49EpmuOiGDxrq/view?usp=drive_link">checkpoint</a></td>
</tr>
<tr>
<td>volo_d1</td>
<td align="left">face_only, age, gender</td>
<td align="left">IMDB-cleaned</td>
<td align="left">4.22</td>
<td align="left">68.68</td>
<td align="left">99.38</td>
<td><a href="https://drive.google.com/file/d/1NlsNEVijX2tjMe8LBb1rI56WB_ADVHeP/view?usp=drive_link">checkpoint</a></td>
</tr>
<tr>
<td>mivolo_d1</td>
<td align="left">face_body, age, gender</td>
<td align="left">IMDB-cleaned</td>
<td align="left">4.24 [face+body]<br>6.87 [body]</td>
<td align="left">68.32 [face+body]<br>46.32 [body]</td>
<td align="left">99.46 [face+body]<br>96.48 [body]</td>
<td><a href="https://drive.google.com/file/d/11i8pKctxz3wVkDBlWKvhYIh7kpVFXSZ4/view?usp=drive_link">model_imdb_cross_person_4.24_99.46.pth.tar</a></td>
</tr>
<tr>
<td>volo_d1</td>
<td align="left">face_only, age</td>
<td align="left">UTKFace</td>
<td align="left">4.23</td>
<td align="left">69.72</td>
<td align="left">-</td>
<td><a href="https://drive.google.com/file/d/1LtDfAJrWrw-QA9U5IuC3_JImbvAQhrJE/view?usp=drive_link">checkpoint</a></td>
</tr>
<tr>
<td>volo_d1</td>
<td align="left">face_only, age, gender</td>
<td align="left">UTKFace</td>
<td align="left">4.23</td>
<td align="left">69.78</td>
<td align="left">97.69</td>
<td><a href="https://drive.google.com/file/d/1hKFmIR6fjHMevm-a9uPEAkDLrTAh-W4D/view?usp=drive_link">checkpoint</a></td>
</tr>
<tr>
<td>mivolo_d1</td>
<td align="left">face_body, age, gender</td>
<td align="left">Lagenda</td>
<td align="left">3.99 [face+body]</td>
<td align="left">71.27 [face+body]</td>
<td align="left">97.36 [face+body]</td>
<td><a href="https://huggingface.co/spaces/iitolstykh/demo">demo</a></td>
</tr>
<tr>
<td>mivolov2_d1_384x384</td>
<td align="left">face_body, age, gender</td>
<td align="left">Lagenda</td>
<td align="left">3.65 [face+body]</td>
<td align="left">74.48 [face+body]</td>
<td align="left">97.99 [face+body]</td>
<td><a href="https://t.me/AnyAgeBot">telegram bot</a></td>
</tr>
</table>
## MiVOLO regression benchmarks
Gender & Age recognition performance.
Use [valid_age_gender.sh](scripts/valid_age_gender.sh) to reproduce results with our checkpoints.
<table style="margin: auto">
<tr>
<th align="left">Model</th>
<th align="left" style="color:LightBlue">Type</th>
<th align="left">Train Dataset</th>
<th align="left">Test Dataset</th>
<th align="left">Age MAE</th>
<th align="left">Age CS@5</th>
<th align="left">Gender Accuracy</th>
<th align="left">download</th>
</tr>
<tr>
<td>mivolo_d1</td>
<td align="left">face_body, age, gender</td>
<td align="left">Lagenda</td>
<td align="left">AgeDB</td>
<td align="left">5.55 [face]</td>
<td align="left">55.08 [face]</td>
<td align="left">98.3 [face]</td>
<td><a href="https://huggingface.co/spaces/iitolstykh/demo">demo</a></td>
</tr>
<tr>
<td>mivolo_d1</td>
<td align="left">face_body, age, gender</td>
<td align="left">IMDB-cleaned</td>
<td align="left">AgeDB</td>
<td align="left">5.58 [face]</td>
<td align="left">55.54 [face]</td>
<td align="left">97.93 [face]</td>
<td><a href="https://drive.google.com/file/d/11i8pKctxz3wVkDBlWKvhYIh7kpVFXSZ4/view?usp=drive_link">model_imdb_cross_person_4.24_99.46.pth.tar</a></td>
</tr>
</table>
## MiVOLO classification benchmarks
Gender & Age recognition performance.
<table style="margin: auto">
<tr>
<th align="left">Model</th>
<th align="left" style="color:LightBlue">Type</th>
<th align="left">Train Dataset</th>
<th align="left">Test Dataset</th>
<th align="left">Age Accuracy</th>
<th align="left">Gender Accuracy</th>
</tr>
<tr>
<td>mivolo_d1</td>
<td align="left">face_body, age, gender</td>
<td align="left">Lagenda</td>
<td align="left">FairFace</td>
<td align="left">61.07 [face+body]</td>
<td align="left">95.73 [face+body]</td>
</tr>
<tr>
<td>mivolo_d1</td>
<td align="left">face_body, age, gender</td>
<td align="left">Lagenda</td>
<td align="left">Adience</td>
<td align="left">68.69 [face]</td>
<td align="left">96.51[face]</td>
</tr>
<tr>
<td>mivolov2_d1_384</td>
<td align="left">face_body, age, gender</td>
<td align="left">Lagenda</td>
<td align="left">Adience</td>
<td align="left">69.43 [face]</td>
<td align="left">97.39[face]</td>
</tr>
</table>
## Dataset
**Please, [cite our papers](#citing) if you use any this data!**
- Lagenda dataset: [images](https://drive.google.com/file/d/1QXO0NlkABPZT6x1_0Uc2i6KAtdcrpTbG/view?usp=sharing) and [annotation](https://drive.google.com/file/d/1mNYjYFb3MuKg-OL1UISoYsKObMUllbJx/view?usp=sharing).
- IMDB-clean: follow [these instructions](https://github.com/yiminglin-ai/imdb-clean) to get images and [download](https://drive.google.com/file/d/17uEqyU3uQ5trWZ5vRJKzh41yeuDe5hyL/view?usp=sharing) our annotations.
- UTK dataset: [origin full images](https://susanqq.github.io/UTKFace/) and our annotation: [split from the article](https://drive.google.com/file/d/1Fo1vPWrKtC5bPtnnVWNTdD4ZTKRXL9kv/view?usp=sharing), [random full split](https://drive.google.com/file/d/177AV631C3SIfi5nrmZA8CEihIt29cznJ/view?usp=sharing).
- Adience dataset: follow [these instructions](https://talhassner.github.io/home/projects/Adience/Adience-data.html) to get images and [download](https://drive.google.com/file/d/1wS1Q4FpksxnCR88A1tGLsLIr91xHwcVv/view?usp=sharing) our annotations.
<details>
<summary>Click to expand!</summary>
After downloading them, your `data` directory should look something like this:
```console
data
└── Adience
├── annotations (folder with our annotations)
├── aligned (will not be used)
├── faces
├── fold_0_data.txt
├── fold_1_data.txt
├── fold_2_data.txt
├── fold_3_data.txt
└── fold_4_data.txt
```
We use coarse aligned images from `faces/` dir.
Using our detector we found a face bbox for each image (see [tools/prepare_adience.py](tools/prepare_adience.py)).
This dataset has five folds. The performance metric is accuracy on five-fold cross validation.
| images before removal | fold 0 | fold 1 | fold 2 | fold 3 | fold 4 |
| --------------------- | ------ | ------ | ------ | ------ | ------ |
| 19,370 | 4,484 | 3,730 | 3,894 | 3,446 | 3,816 |
Not complete data
| only age not found | only gender not found | SUM |
| ------------------ | --------------------- | ------------- |
| 40 | 1170 | 1,210 (6.2 %) |
Removed data
| failed to process image | age and gender not found | SUM |
| ----------------------- | ------------------------ | ----------- |
| 0 | 708 | 708 (3.6 %) |
Genders
| female | male |
| ------ | ----- |
| 9,372 | 8,120 |
Ages (8 classes) after mapping to not intersected ages intervals
| 0-2 | 4-6 | 8-12 | 15-20 | 25-32 | 38-43 | 48-53 | 60-100 |
| ----- | ----- | ----- | ----- | ----- | ----- | ----- | ------ |
| 2,509 | 2,140 | 2,293 | 1,791 | 5,589 | 2,490 | 909 | 901 |
</details>
- FairFace dataset: follow [these instructions](https://github.com/joojs/fairface) to get images and [download](https://drive.google.com/file/d/1EdY30A1SQmox96Y39VhBxdgALYhbkzdm/view?usp=drive_link) our annotations.
<details>
<summary>Click to expand!</summary>
After downloading them, your `data` directory should look something like this:
```console
data
└── FairFace
├── annotations (folder with our annotations)
├── fairface-img-margin025-trainval (will not be used)
├── train
├── val
├── fairface-img-margin125-trainval
├── train
├── val
├── fairface_label_train.csv
├── fairface_label_val.csv
```
We use aligned images from `fairface-img-margin125-trainval/` dir.
Using our detector we found a face bbox for each image and added a person bbox if it was possible (see [tools/prepare_fairface.py](tools/prepare_fairface.py)).
This dataset has 2 splits: train and val. The performance metric is accuracy on validation.
| images train | images val |
| ------------ | ---------- |
| 86,744 | 10,954 |
Genders for **validation**
| female | male |
| ------ | ----- |
| 5,162 | 5,792 |
Ages for **validation** (9 classes):
| 0-2 | 3-9 | 10-19 | 20-29 | 30-39 | 40-49 | 50-59 | 60-69 | 70+ |
| --- | ----- | ----- | ----- | ----- | ----- | ----- | ----- | --- |
| 199 | 1,356 | 1,181 | 3,300 | 2,330 | 1,353 | 796 | 321 | 118 |
</details>
- AgeDB dataset: follow [these instructions](https://ibug.doc.ic.ac.uk/resources/agedb/) to get images and [download](https://drive.google.com/file/d/1Dp72BUlAsyUKeSoyE_DOsFRS1x6ZBJen/view) our annotations.
<details>
<summary>Click to expand!</summary>
**Ages**: 1 - 101
**Genders**: 9788 faces of `M`, 6700 faces of `F`
| images 0 | images 1 | images 2 | images 3 | images 4 | images 5 | images 6 | images 7 | images 8 | images 9 |
|----------|----------|----------|----------|----------|----------|----------|----------|----------|----------|
| 1701 | 1721 | 1615 | 1619 | 1626 | 1643 | 1634 | 1596 | 1676 | 1657 |
Data splits were taken from [here](https://github.com/paplhjak/Facial-Age-Estimation-Benchmark-Databases)
!! **All splits(all dataset) were used for models evaluation.**
</details>
## Install
Install pytorch 1.13+ and other requirements.
```
pip install -r requirements.txt
pip install .
```
## Demo
1. [Download](https://drive.google.com/file/d/1CGNCkZQNj5WkP3rLpENWAOgrBQkUWRdw/view) body + face detector model to `models/yolov8x_person_face.pt`
2. [Download](https://drive.google.com/file/d/11i8pKctxz3wVkDBlWKvhYIh7kpVFXSZ4/view) mivolo checkpoint to `models/mivolo_imbd.pth.tar`
```bash
wget https://variety.com/wp-content/uploads/2023/04/MCDNOHA_SP001.jpg -O jennifer_lawrence.jpg
python3 demo.py \
--input "jennifer_lawrence.jpg" \
--output "output" \
--detector-weights "models/yolov8x_person_face.pt " \
--checkpoint "models/mivolo_imbd.pth.tar" \
--device "cuda:0" \
--with-persons \
--draw
```
To run demo for a youtube video:
```bash
python3 demo.py \
--input "https://www.youtube.com/shorts/pVh32k0hGEI" \
--output "output" \
--detector-weights "models/yolov8x_person_face.pt" \
--checkpoint "models/mivolo_imbd.pth.tar" \
--device "cuda:0" \
--draw \
--with-persons
```
## Validation
To reproduce validation metrics:
1. Download prepared annotations for imbd-clean / utk / adience / lagenda / fairface.
2. Download checkpoint
3. Run validation:
```bash
python3 eval_pretrained.py \
--dataset_images /path/to/dataset/utk/images \
--dataset_annotations /path/to/dataset/utk/annotation \
--dataset_name utk \
--split valid \
--batch-size 512 \
--checkpoint models/mivolo_imbd.pth.tar \
--half \
--with-persons \
--device "cuda:0"
````
Supported dataset names: "utk", "imdb", "lagenda", "fairface", "adience".
## Changelog
[CHANGELOG.md](CHANGELOG.md)
## ONNX and TensorRT export
As of now (11.08.2023), while ONNX export is technically feasible, it is not advisable due to the poor performance of the resulting model with batch processing.
**TensorRT** and **OpenVINO** export is impossible due to its lack of support for col2im.
If you remain absolutely committed to utilizing ONNX export, you can refer to [these instructions](https://github.com/WildChlamydia/MiVOLO/issues/14#issuecomment-1675245889).
The most highly recommended export method at present **is using TorchScript**. You can achieve this with a single line of code:
```python
torch.jit.trace(model)
```
This approach provides you with a model that maintains its original speed and only requires a single file for usage, eliminating the need for additional code.
## License
Please, see [here](./license)
## Citing
If you use our models, code or dataset, we kindly request you to cite the following paper and give repository a :star:
```bibtex
@article{mivolo2023,
Author = {Maksim Kuprashevich and Irina Tolstykh},
Title = {MiVOLO: Multi-input Transformer for Age and Gender Estimation},
Year = {2023},
Eprint = {arXiv:2307.04616},
}
```
```bibtex
@article{mivolo2024,
Author = {Maksim Kuprashevich and Grigorii Alekseenko and Irina Tolstykh},
Title = {Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation},
Year = {2024},
Eprint = {arXiv:2403.02302},
}
```
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