Ultralytics YOLOv8, developed by Ultralytics, is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection, image segmentation and image classification tasks.
- ✅ YOLOv11-n (face) trained on WIDERFace [08.10]
- ✅ YOLOv11-s (face) trained on WIDERFace [08.10]
- ✅ YOLOv11-m (face) trained on WIDERFace [09.10]
- ✅ YOLOv10-n (face) trained on WIDERFace [10.10]
- ✅ YOLOv10-s (face) trained on WIDERFace [10.10]
- ✅ YOLOv10-m (face) trained on WIDERFace [11.10]
# clone repo
git clone https://github.com/akanametov/yolo-face
# pip install required packages
pip install ultralytics
# go to code folder
cd yolo-faceyolov11n-face.pt
yolov11s-face.pt
yolov11m-face.pt
yolov10n-face.pt
yolov10s-face.pt
yolov10m-face.pt
yolov8n-face.pt
yolov8m-face.pt
yolov8l-face.pt
yolov8n-football.pt
yolov8m-football.pt
yolov8n-parking.pt
yolov8m-parking.pt
yolov8n-drone.pt
yolov8m-drone.pt
yolov11n-face.onnx
yolov10n-face.onnx
yolov8n-face.onnx
To convert models to .onnx format:
# Install ultralytics
pip install ultralytics
# Convert with command
yolo export model=yolov10n-face.pt format=onnx
On image:
yolo task=detect mode=predict model=yolov11n-face.pt conf=0.25 imgsz=1280 line_thickness=1 max_det=1000 source=examples/face.jpg
PR curve:
Losses and mAP:
Confusion matrix:
Data preparation
-
Download dataset:
-
Download pretrained yolo11n.pt model.
Single GPU training
# train model
yolo task=detect \
mode=train \
model=yolo11n.pt \
data=datasets/data.yaml \
epochs=100 \
batch=32 \
imgsz=640On image:
yolo task=detect mode=predict model=yolov8n-face.pt conf=0.25 imgsz=1280 line_thickness=1 max_det=1000 source=examples/face.jpg
PR curve:
Losses and mAP:
Confusion matrix:
Data preparation
-
Download dataset:
-
Download pretrained yolov8n.pt model.
Single GPU training
# train model
yolo task=detect \
mode=train \
model=yolov8n.pt \
data=datasets/data.yaml \
epochs=100 \
imgsz=640On image:
yolo task=detect mode=predict model=yolov8n-face.pt conf=0.25 imgsz=1280 line_thickness=1 max_det=1000 source=examples/person.jpg
PR curve:
Losses and mAP:
Data preparation
-
Download dataset:
-
Download pretrained yolov8n.pt model.
Single GPU training
# train model
yolo task=detect \
mode=train \
model=yolov8n.pt \
data=datasets/data.yaml \
epochs=100 \
imgsz=640On image:
yolo task=detect mode=predict model=yolov8m-football.pt conf=0.25 imgsz=1280 line_thickness=1 source=examples/football.jpg
PR curve:
Losses and mAP:
Confusion matrix:
Data preparation
-
Download dataset:
-
Download pretrained yolov8m.pt model.
Single GPU training
# train model
yolo task=detect \
mode=train \
model=yolov8m.pt \
data=datasets/data.yaml \
epochs=120 \
imgsz=960On image:
yolo task=detect mode=predict model=yolov8m-parking.pt conf=0.25 imgsz=1280 line_thickness=1 source=examples/parking.jpg
PR curve:
Losses and mAP:
Confusion matrix:
Data preparation
-
Download dataset:
-
Download pretrained yolov8m.pt model.
Single GPU training
# train model
yolo task=detect \
mode=train \
model=yolov8m.pt \
data=datasets/data.yaml \
epochs=10 \
batch=32 \
imgsz=640On image:
yolo task=detect mode=predict model=yolov8m-drone.pt conf=0.25 imgsz=1280 line_thickness=1 source=examples/drone.jpg
PR curve:
Losses and mAP:
Confusion matrix:
Data preparation
-
Download dataset:
-
Download pretrained yolov8m.pt model.
Single GPU training
# train model
yolo task=detect \
mode=train \
model=yolov8m.pt \
data=datasets/data.yaml \
epochs=100 \
imgsz=640YOLOv8 is available under two different licenses:
- GPL-3.0 License: See LICENSE file for details.
- Enterprise License: Provides greater flexibility for commercial product development without the open-source requirements of GPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at Ultralytics Licensing.
For YOLOv8 bugs and feature requests please visit GitHub Issues. For professional support please Contact Us.
