---
layout: default
title: Home
nav_order: 1
has_children: false
annotations_creators:
- no-annotation
language:
- en
language_creators:
- found
pretty_name: DiffusionFER
size_categories:
- n<500MB
source_datasets:
- original
license: cc0-1.0
tags:
- stable diffusion
- prompt engineering
- prompts
- research paper
- facial expression recognition
- emotion recognition
task_categories:
- text-to-image
task_ids:
- image-captioning
- face-detection
---
## Dataset Description
- **Homepage:** [DiffusionFER homepage](https://kdhht2334.github.io/)
- **Repository:** [DiffusionFER repository](https://github.com/kdhht2334/Facial-Expression-Recognition-Zoo)
- **Distribution:** [DiffusionFER Hugging Face Dataset](https://huggingface.co/datasets/FER-Universe/DiffusionFER)
- **Point of Contact:** [Daeha Kim](mailto:kdhht5022@gmail.com)
### Summary
DiffusionFER is the large-scale text-to-image prompt database for face-related tasks. It contains about **1M(ongoing)** images generated by [Stable Diffusion](https://github.com/camenduru/stable-diffusion-webui-colab) using prompt(s) and other parameters.
DiffusionFER is available at [π€ Hugging Face Dataset](https://huggingface.co/datasets/FER-Universe/DiffusionFER).
### Downstream Tasks and Leaderboards
This DiffusionFER dataset can be utilized for the following downstream tasks.
- Face detection
- Facial expression recognition
- Text-to-emotion prompting
In addition, the virtual subjects included in this dataset provide opportunities to perform various vision tasks related to face privacy.
### Data Loading
DiffusionFER can be loaded via both Python and Git. Please refer Hugging Face [`Datasets`](https://huggingface.co/docs/datasets/quickstart).
```python
from datasets import load_dataset
dataset = load_dataset("FER-Universe/DiffusionFER")
```
```bash
git lfs install
git clone https://huggingface.co/datasets/FER-Universe/DiffusionFER
```
### Pre-trained model
You can easily download and use pre-trained __Swin Transformer__ model with the `Diffusion_Emotion_S` dataset.
Later, Transformer models with the `Diffusion_Emotion_M` or `Diffusion_Emotion_L` will be released.
```python
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
extractor = AutoFeatureExtractor.from_pretrained("kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176")
model = AutoModelForImageClassification.from_pretrained("kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176")
```
Or just clone the model repo
```bash
git lfs install
git clone https://huggingface.co/kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176
```
- Quick links: [huggingface model documentation](https://huggingface.co/docs/transformers/main/en/model_doc/swin#transformers.SwinForImageClassification)
### Sample Gallery
βΌHappy
βΌAngry
### Subsets
DiffusionFER supports a total of three distinct splits. And, each split additionally provides a face region cropped by [face detector](https://github.com/timesler/facenet-pytorch).
- DifussionEmotion_S (small), DifussionEmotion_M (medium), DifussionEmotion_L (large).
|Subset|Num of Images|Size|Image Directory |
|:--|--:|--:|--:|
|DifussionEmotion_S (original) | 1.5K | 647M | `DifussionEmotion_S/` |
|DifussionEmotion_S (cropped) | 1.5K | 322M | `DiffusionEmotion_S_cropped/` |
|DifussionEmotion_M (original) | N/A | N/A | `DifussionEmotion_M/` |
|DifussionEmotion_M (cropped) | N/A | N/A | `DiffusionEmotion_M_cropped/` |
|DifussionEmotion_L (original) | N/A | N/A | `DifussionEmotion_L/` |
|DifussionEmotion_L (cropped) | N/A | N/A | `DiffusionEmotion_L_cropped/` |
## Dataset Structure
We provide DiffusionFER using a modular file structure. `DiffusionEmotion_S`, the smallest scale, contains about 1,500 images and is divided into folders of a total of 7 emotion classes. The class labels of all these images are included in `dataset_sheet.csv`.
- In `dataset_sheet.csv`, not only 7-emotion class but also _valence-arousal_ value are annotated.
```bash
# Small version of DB
./
βββ DifussionEmotion_S
βΒ Β βββ angry
βΒ Β βΒ Β βββ aaaaaaaa_6.png
βΒ Β βΒ Β βββ andtcvhp_6.png
βΒ Β βΒ Β βββ azikakjh_6.png
βΒ Β βΒ Β βββ [...]
βΒ Β βββ fear
βΒ Β βββ happy
βΒ Β βββ [...]
βΒ Β βββ surprise
βββ dataset_sheet.csv
```
- Middle size DB will be uploaded soon.
```bash
# Medium version of DB
(ongoing)
```
- TBD
```bash
# Large version of DB
(ongoing)
```
### Prompt Format
Basic format is as follows: "`Emotion`, `Race` `Age` style, a realistic portrait of `Style` `Gender`, upper body, `Others`".
- ex) one person, neutral emotion, white middle-aged style, a realistic portrait of man, upper body
Examples of format categories are listed in the table below.
| Category | Prompt(s) |
| --- | --- |
| `Emotion` | neutral emotion
happy emotion, with open mouth, smiley
sad emotion, with tears, lowered head, droopy eyebrows
surprise emotion, with open mouth, big eyes
fear emotion, scared, haunted
disgust emotion, frown, angry expression with open mouth
angry emotion, with open mouth, frown eyebrow, fierce, furious |
| `Race` | white
black
latin |
| `Age` | teen
middle-aged
old |
| `Gender` | man
woman |
| `Style` | gentle
handsome
pretty
cute
mature
punky
freckles
beautiful crystal eyes
big eyes
small nose
... |
| `Others` | 4K
8K
cyberpunk
camping
ancient
medieval Europe
... |
### Prompt Engineering
You can improve the performance and quality of generating default prompts with the settings below.
```
{
"negative prompt": "sketches, (worst quality:2), (low quality:2), (normal quality:2), lowres, normal quality, ((monochrome)), ((grayscale)), skin spots, acnes, skin blemishes, backlight, (duplicate:1.331), (morbid:1.21), (mutilated:1.21), mutated hands, (poorly drawn hands:1.331), (bad anatomy:1.21), (bad proportions:1.331), extra limbs, (disfigured:1.331), (missing arms:1.331), (extra legs:1.331), (fused fingers:1.61051), (too many fingers:1.61051), (unclear eyes:1.331), bad hands, missing fingers, extra digit",
"steps": 50,
"sampling method": "DPM++ 2M Karras"
"Width": "512",
"Height": "512",
"CFG scale": 12.0,
"seed": -1,
}
```
### Annotations
The DiffusionFER contains annotation process both 7-emotion classes and valence-arousal values.
#### Annotation process
This process was carried out inspired by the theory of the two research papers below.
- JA Russell, [A circumplex model of affect](https://d1wqtxts1xzle7.cloudfront.net/38425675/Russell1980-libre.pdf?1439132613=&response-content-disposition=inline%3B+filename%3DRussell1980.pdf&Expires=1678595455&Signature=UtbPsezND6w8vbISBiuL-ECk6hDI0etLcJSE7kJMC~hAkMSu9YyQcPKdVpdHSSq7idfcQ~eEKsqptvYpy0199DX0gi-nHJwhsciahC-zgDwylEUo6ykhP6Ab8VWCOW-DM21jHNvbYLQf7Pwi66fGvm~5bAXPc1o4HHpQpk-Cr7b0tW9lYnl3qgLoVeIICg6FLu0elbtVztgH5OS1uL6V~QhiP2PCwZf~WCHuJRQrWdPt5Kuco0lsNr1Qikk1~d7HY3ZcUTRZcMNDdem8XAFDH~ak3QER6Ml~JDkNFcLuygz~tjL4CdScVhByeAuMe3juyijtBFtYWH2h30iRkUDalg__&Key-Pair-Id=APKAJLOHF5GGSLRBV4ZA)
- A Mollahosseini et al., [AffectNet](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8013713&casa_token=C3QmhmiB6Y8AAAAA:1CiUll0bhIq06M17YwFIvxuse7GOosEN9G1A8vxVzR8Vb5eaFp6ERIjg7xhSIQlf008KLsfJ-w&tag=1)
#### Who are the annotators?
[Daeha Kim](mailto:kdhht5022@gmail.com) and [Dohee Kang](mailto:asrs777@naver.com)
## Additional Information
### Dataset Curators
DiffusionFER is created by [Daeha Kim](https://kdhht2334.github.io/) and [Dohee Kang](https://github.com/KangDohee2270).
### Acknowledgments
This repository is heavily inspired by [DiffusionDB](https://huggingface.co/datasets/poloclub/diffusiondb), with some format references. Thank you for your interest in [DiffusionDB](https://huggingface.co/datasets/poloclub/diffusiondb).
### Licensing Information
The DiffusionFER is available under the [CC0 1.0 License](https://creativecommons.org/publicdomain/zero/1.0/). NOTE: The primary purpose of this dataset is research. We are not responsible if you take any other action using this dataset.
### Contributions
If you have any questions, feel free to [open an issue](https://github.com/kdhht2334/Facial-Expression-Recognition-Zoo/issues/new) or contact [Daeha Kim](https://kdhht2334.github.io/).