https://github.com/YOLOSHOW/YOLOSHOW
YOLO SHOW - YOLOv11 / YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR YOLO GUI based on Pyside6
https://github.com/YOLOSHOW/YOLOSHOW
gui rtdetr yolo yolo-show yologui yolov11 yolov5 yolov7 yolov8 yolov9
Last synced: about 1 month ago
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YOLO SHOW - YOLOv11 / YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR YOLO GUI based on Pyside6
- Host: GitHub
- URL: https://github.com/YOLOSHOW/YOLOSHOW
- Owner: YOLOSHOW
- License: agpl-3.0
- Created: 2024-02-18T03:33:48.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-10-31T09:17:34.000Z (7 months ago)
- Last Synced: 2024-10-31T10:18:44.522Z (7 months ago)
- Topics: gui, rtdetr, yolo, yolo-show, yologui, yolov11, yolov5, yolov7, yolov8, yolov9
- Language: Python
- Homepage: https://swimmingliu.cn/posts/diary/yoloshow
- Size: 264 MB
- Stars: 293
- Watchers: 2
- Forks: 29
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-yolo-object-detection - JSwimmingLiu/YOLOSHOW - YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR GUI based on Pyside6.[swimmingliu.cn/posts/diary/yoloshow](https://swimmingliu.cn/posts/diary/yoloshow) (Applications)
- awesome-yolo-object-detection - JSwimmingLiu/YOLOSHOW - YOLOv10 / YOLOv9 / YOLOv8 / YOLOv7 / YOLOv5 / RTDETR GUI based on Pyside6.[swimmingliu.cn/posts/diary/yoloshow](https://swimmingliu.cn/posts/diary/yoloshow) (Applications)
README
# YOLOSHOW - YOLOv5 / YOLOv7 / YOLOv8 / YOLOv9 / YOLOv10 / YOLOv11 / RTDETR / SAM / MobileSAM / FastSAM GUI based on Pyside6
## Introduction
***YOLOSHOW*** is a graphical user interface (GUI) application embed with `YOLOv5` `YOLOv7` `YOLOv8` `YOLOv9` `YOLOv10` `YOLOv11` `RT-DETR` `SAM` `MobileSAM` `FastSAM` algorithm.
English | 简体中文
## Demo Video
`YOLOSHOW v1.x` : [YOLOSHOW-YOLOv9/YOLOv8/YOLOv7/YOLOv5/RTDETR GUI](https://www.bilibili.com/video/BV1BC411x7fW)
`YOLOSHOW v2.x` : [YOLOSHOWv2.0-YOLOv9/YOLOv8/YOLOv7/YOLOv5/RTDETR GUI](https://www.bilibili.com/video/BV1ZD421E7m3)
## Todo List
- [x] Add `YOLOv9` `YOLOv10` `RT-DETR` `YOLOv11` `SAM` `MobileSAM` `FastSAM` Algorithm
- [x] Support Instance Segmentation （ `YOLOv5` `YOLOv8` `YOLOv11` `SAM` `MobileSAM` `FastSAM`）
- [x] Support Pose Estimation （ `YOLOv8` `YOLOv11`）
- [x] Support Oriented Bounding Boxes ( `YOLOv8` `YOLOv11` )
- [x] Support Http Protocol in `RTSP` Function ( `Single` Mode )
- [x] Add Model Comparison Mode（VS Mode）
- [x] Support Dragging File Input
- [ ] Tracking & Counting ( `Industrialization` )
## Functions
### 1. Support Image / Video / Webcam / Folder (Batch) / IPCam Object Detection
Choose Image / Video / Webcam / Folder (Batch) / IPCam in the menu bar on the left to detect objects.
### 2. Change Models / Hyper Parameters dynamically
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
### 3. Loading Model Automatically
Our program will automatically detect `pt` files including [YOLOv5 Models](https://github.com/ultralytics/yolov5/releases) / [YOLOv7 Models](https://github.com/WongKinYiu/yolov7/releases/) / [YOLOv8 Models](https://github.com/ultralytics/assets/releases/) / [YOLOv9 Models](https://github.com/WongKinYiu/yolov9/releases/) / [YOLOv10 Models](https://github.com/THU-MIG/yolov10/releases/) / [YOLOv11 Models](https://github.com/ultralytics/assets/releases/) / [RT-DETR Models](https://github.com/ultralytics/assets/releases/) / [SAM Models](https://github.com/ultralytics/assets/releases/) / [MobileSAM Models](https://github.com/ultralytics/assets/releases/) / [FastSAM Models](https://github.com/ultralytics/assets/releases/) 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`)
### 4. Loading Configures
1. After startup, the program will automatically loading the last configure parameters.
2. After closedown, the program will save the changed configure parameters.
### 5. Save Results
If you need Save results, please click `Save Mode` before detection. Then you can save your detection results in selected path.
### 6. Support Object Detection, Instance Segmentation and Pose Estimation
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.
### 7. Support Model Comparison among Object Detection, Instance Segmentation, Pose Estimation and Oriented Bounding Box
From ***YOLOSHOW v3.0***，our work supports compare model performance among Object Detection, Instance Segmentation, Pose Estimation and Oriented Bounding Box.
## Preparation
### Experimental environment
```Shell
OS : Windows 11
CPU : Intel(R) Core(TM) i7-10750H CPU @2.60GHz 2.59 GHz
GPU : NVIDIA GeForce GTX 1660Ti 6GB
```
### 1. Create virtual environment
create a virtual environment equipped with python version 3.9, then activate environment.
```shell
conda create -n yoloshow python=3.9
conda activate yoloshow
```
### 2. Install Pytorch frame
```shell
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 [](https://pytorch.org/)
### 3. Install dependency package
Switch the path to the location of the program
```shell
cd {the location of the program}
```
Install dependency package of program
```shell
pip install -r requirements.txt -i https://pypi.tuna.tsinghua.edu.cn/simple
```
### 4. Add Font
#### Windows User
Copy all font files `*.ttf` in `fonts` folder into `C:\Windows\Fonts`
#### Linux User
```shell
mkdir -p ~/.local/share/fonts
sudo cp fonts/Shojumaru-Regular.ttf ~/.local/share/fonts/
sudo fc-cache -fv
```
#### MacOS User
The MacBook is so expensive that I cannot afford it, please install `.ttf` by yourself. 😂
### 5. Run Program
```shell
python main.py
```
## Frames
[](https://www.python.org/)[](https://pytorch.org/)[](https://doc.qt.io/qtforpython-6/PySide6/QtWidgets/index.html)
## Reference
### YOLO Algorithm
[YOLOv5](https://github.com/ultralytics/yolov5) [YOLOv7](https://github.com/WongKinYiu/yolov7) [YOLOv8 / YOLOv11 / RT-DETR / SAM / MobileSAM / FastSAM](https://github.com/ultralytics/ultralytics) [YOLOv9](https://github.com/WongKinYiu/yolov9) [YOLOv10](https://github.com/THU-MIG/yolov10)
### YOLO Graphical User Interface
[YOLOSIDE](https://github.com/Jai-wei/YOLOv8-PySide6-GUI) [PyQt-Fluent-Widgets](https://github.com/zhiyiYo/PyQt-Fluent-Widgets)