Datasets:
image
imagewidth (px) 224
512
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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 using prompt(s) and other parameters.
DiffusionFER is available at π€ Hugging Face Dataset.
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
.
from datasets import load_dataset
dataset = load_dataset("FER-Universe/DiffusionFER")
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.
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
git lfs install
git clone https://huggingface.co/kdhht2334/autotrain-diffusion-emotion-facial-expression-recognition-40429105176
- Quick links: huggingface model documentation
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.
- 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.
# Small version of DB
./
βββ DifussionEmotion_S
β βββ angry
β β βββ aaaaaaaa_6.png
β β βββ andtcvhp_6.png
β β βββ azikakjh_6.png
β β βββ [...]
β βββ fear
β βββ happy
β βββ [...]
β βββ surprise
βββ dataset_sheet.csv
- Middle size DB will be uploaded soon.
# Medium version of DB
(ongoing)
- TBD
# 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
- A Mollahosseini et al., AffectNet
Who are the annotators?
Daeha Kim and Dohee Kang
Additional Information
Dataset Curators
DiffusionFER is created by Daeha Kim and Dohee Kang.
Acknowledgments
This repository is heavily inspired by DiffusionDB, with some format references. Thank you for your interest in DiffusionDB.
Licensing Information
The DiffusionFER is available under the CC0 1.0 License. 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 or contact Daeha Kim.
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