YOLOSHOW is a graphical user interface (GUI) application embed with YOLOv5 YOLOv7 YOLOv8 YOLOv9 YOLOv10 YOLOv11 RT-DETR SAM MobileSAM FastSAM algorithm.

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YOLOSHOW v1.x : YOLOSHOW-YOLOv9/YOLOv8/YOLOv7/YOLOv5/RTDETR GUI

YOLOSHOW v2.x : YOLOSHOWv2.0-YOLOv9/YOLOv8/YOLOv7/YOLOv5/RTDETR GUI

• Add YOLOv9 YOLOv10 RT-DETR YOLOv11 SAM MobileSAM FastSAM Algorithm
• Support Instance Segmentation （ YOLOv5 YOLOv8 YOLOv11 SAM MobileSAM FastSAM）
• Support Pose Estimation （ YOLOv8 YOLOv11）
• Support Oriented Bounding Boxes ( YOLOv8 YOLOv11 )
• Support Http Protocol in RTSP Function ( Single Mode )
• Add Model Comparison Mode（VS Mode）
• Support Dragging File Input
• Tracking & Counting ( Industrialization )

Choose Image / Video / Webcam / Folder (Batch) / IPCam in the menu bar on the left to detect objects.

When the program is running to detect targets, you can change models / hyper Parameters

1. Support changing model in YOLOv5 / YOLOv7 / YOLOv8 / YOLOv9 / YOLOv10 / YOLOv11 / RTDETR / YOLOv5-seg / YOLOv8-seg YOLOv11-seg / YOLOv8-pose / YOLOv11-pose / YOLOv8-obb / YOLOv11-obb / SAM / MobileSAM / FastSAM dynamically
2. Support changing IOU / Confidence / Delay time / line thickness dynamically

Our program will automatically detect pt files including YOLOv5 Models / YOLOv7 Models / YOLOv8 Models / YOLOv9 Models / YOLOv10 Models / YOLOv11 Models / RT-DETR Models / SAM Models / MobileSAM Models / FastSAM Models that were previously added to the ptfiles folder.

If you need add the new pt file, please click Import Model button in Settings box to select your pt file. Then our program will put it into ptfiles folder.

Notice :

1. All pt files are named including yolov5 / yolov7 / yolov8 / yolov9 / yolov10 / yolo11 / rtdetr / sam / samv2 / mobilesam / fastsam. (e.g. yolov8-test.pt)
2. If it is a pt file of segmentation mode, please name it including yolov5n-seg / yolov8s-seg / yolo11-seg . (e.g. yolov8n-seg-test.pt)
3. If it is a pt file of pose estimation mode, please name it including yolov8n-pose / yolo11n-pose . (e.g. yolov8n-pose-test.pt)
4. If it is a pt file of oriented bounding box mode, please name it including yolov8n-obb / yolo11n-obb . (e.g. yolov8n-obb-test.pt)

1. After startup, the program will automatically loading the last configure parameters.
2. After closedown, the program will save the changed configure parameters.

If you need Save results, please click Save Mode before detection. Then you can save your detection results in selected path.

From YOLOSHOW v3.0，our work supports both Object Detection , Instance Segmentation, Pose Estimation and Oriented Bounding Box. Meanwhile, it also supports task switching between different versions，such as switching from YOLOv5 Object Detection task to YOLOv8 Instance Segmentation task.

From YOLOSHOW v3.0，our work supports compare model performance among Object Detection, Instance Segmentation, Pose Estimation and Oriented Bounding Box.

OS : Windows 11 
CPU : Intel(R) Core(TM) i7-10750H CPU @2.60GHz 2.59 GHz
GPU : NVIDIA GeForce GTX 1660Ti 6GB

create a virtual environment equipped with python version 3.9, then activate environment.

conda create -n yoloshow python=3.9
conda activate yoloshow

Windows: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118
Linux: pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu118

Change other pytorch version in

Switch the path to the location of the program

cd {the location of the program}

Install dependency package of program

pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple

Copy all font files *.ttf in fonts folder into C:\Windows\Fonts

mkdir -p ~/.local/share/fonts
sudo cp fonts/Shojumaru-Regular.ttf ~/.local/share/fonts/
sudo fc-cache -fv

The MacBook is so expensive that I cannot afford it, please install .ttf by yourself. 😂

python main.py

YOLOv5 YOLOv7 YOLOv8 / YOLOv11 / RT-DETR / SAM / MobileSAM / FastSAM YOLOv9 YOLOv10

YOLOSIDE PyQt-Fluent-Widgets