Face Detection Dataset
A set of high quality images annotated for face detection, including two label formats: pixel and YOLO. Ideal for training vision AI models.
Description
The dataset Face Detection Dataset contains 16,700 images carefully selected and annotated for face detection. Each image is accompanied by two types of annotations: in raw format (pixel coordinates) and in YOLO format (standardized coordinates), which makes it compatible with the most used frameworks in computer vision.
What is this dataset for?
- Train deep learning face detection models (e.g. YOLO, SSD, Faster R-CNN)
- Testing the accuracy and robustness of vision models
- Create security applications, biometrics or facial recognition
Can it be enriched or improved?
Yes, the dataset can be enriched with additional labels (age, emotion, face orientation). It is also possible to add faces in various environments (angle, lighting, occlusion) to increase its robustness. YOLO annotations can be easily converted for other architectures.
🔎 In summary
🧠 Recommended for
- Computer vision students
- Security application developers
- Visual AI researchers
🔧 Compatible tools
- Yolov5/v8
- Roboflow
- OpenCV
- LabelImg
- Ultralytics
💡 Tip
For better performance, use data augmentation techniques (zoom, flip, rotation) during training.
Frequently Asked Questions
Can this dataset be used for video surveillance systems?
Yes, it is particularly suitable for real-time detection tasks, especially with YOLO or SSD.
Is there an annotation of emotions or expressions?
No, only face coordinates are provided. They can be added manually to enrich the dataset.
Is it compatible with Google Colab and Ultralytics YOLO?
Yes, the files are already in YOLO format, which makes it directly usable in Ultralytics or other Colab notebooks.
