This repository supply a user-friendly interactive interface for YOLOv8 with Object Tracking and Counting capability. The interface is powered by Streamlit.
- Feature1: Object detection task.
- Feature2: Multiple detection models.
yolov8n,yolov8s,yolov8m,yolov8l,yolov8x - Feature3: Multiple input formats.
Image,Video,Webcam - Feature4: Multiple Object Tracking and Counting.
You can use This link to try an online version on Streamlit.
# create
python -m venv yolov8-mot-streamlit
# activate
source yolov8-mot-streamlit/bin/activate
git clone https://github.com/monemati/YOLOv8-DeepSORT-Streamlit.git
cd YOLOv8-DeepSORT-Streamlit
# Streamlit dependencies
pip install streamlit
# YOLOv8 dependecies
pip install -e '.[dev]'
Create a directory named weights and create a subdirectory named detection and save the downloaded YOLOv8 object detection weights inside this directory. The weight files can be downloaded from the table below.
| Model | size (pixels) |
mAPval 50-95 |
Speed CPU ONNX (ms) |
Speed A100 TensorRT (ms) |
params (M) |
FLOPs (B) |
|---|---|---|---|---|---|---|
| YOLOv8n | 640 | 37.3 | 80.4 | 0.99 | 3.2 | 8.7 |
| YOLOv8s | 640 | 44.9 | 128.4 | 1.20 | 11.2 | 28.6 |
| YOLOv8m | 640 | 50.2 | 234.7 | 1.83 | 25.9 | 78.9 |
| YOLOv8l | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| YOLOv8x | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |
streamlit run app.py
Then will start the Streamlit server and open your web browser to the default Streamlit page automatically. For Object Counting, you can choose "Video" from "Select Source" combo box and use "test3.mp4" inside videos folder as an example.
