Quickrun

Quickrun is a software designed for the efficient and high-concurrency deployment of multiple models on the RK3588 platform with RKNN.

Software Framework

Session-Based Design:
- Implements a session concept, allowing the definition of multiple sessions for different task requirements.
- Example: Supports scenarios like charging pile detection, garbage classification, and cliff detection where multiple models share the same camera, e.g., using YOLOv5.
Message Queue for Data Management:
- Uses a message queue to store photo data, preventing frame loss and ensuring efficient concurrency.
- Photo data is collected at 25fps, with a total processing time (pre-processing, inference, post-processing) of 40ms per frame.
Input Processing:
- Handles model input of 640x640 while the camera input is 640x480.
- Decodes the input using cv::imdecode, converts it to RGB format, and leverages RGA for accelerated proportional scaling.
Threaded Model Execution:
- Three independent threads for three sessions to execute models concurrently without interference.

Model Output Customization

For RK3588 YOLOv5 model:
- When converting to ONNX, remove the cat operation in the forward layer.
- Configure the model to output three feature maps: 20x20, 40x40, and 80x80.
- Modify the necessary files: yolo.py and export.py.

Performance Overview

Resource Usage:
- One model uses:
  - 1.2T of the NPU.
  - 40% of the CPU for pre-processing, inference, and post-processing (including frame drawing).
Inference Time:
- Achieves an inference time of 20ms.
CPU Monitoring:
- Use the perf top -p command to view the CPU usage rate, down to specific functions.

Quick Start

Compilation

Run the following command to build the project:

bash build_rk3588_yolov5.sh

Testing

Run the following command to test the deployment:

bash test_rk3588_yolov5.sh

Quickrun Deployment

Modify the following parameters in the code as per your project’s requirements:

#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NUMB_MAX_SIZE 64 #define OBJ_CLASS_NUM 1 #define NMS_THRESH 0.25 #define BOX_THRESH 0.5

Robot Demo Video

View Robot Video on GitHub

机器人视频观看 You can also watch the video below:

Your browser does not support the video tag.

License

Quickrun is licensed under the MIT License by Jim.

Quickrun

Quickrun is a software designed for the efficient and high-concurrency deployment of multiple models on the RK3588 platform with RKNN.

Software Framework

Session-Based Design:
- Implements a session concept, allowing the definition of multiple sessions for different task requirements.
- Example: Supports scenarios like charging pile detection, garbage classification, and cliff detection where multiple models share the same camera, e.g., using YOLOv5.
Message Queue for Data Management:
- Uses a message queue to store photo data, preventing frame loss and ensuring efficient concurrency.
- Photo data is collected at 25fps, with a total processing time (pre-processing, inference, post-processing) of 40ms per frame.
Input Processing:
- Handles model input of 640x640 while the camera input is 640x480.
- Decodes the input using cv::imdecode, converts it to RGB format, and leverages RGA for accelerated proportional scaling.
Threaded Model Execution:
- Three independent threads for three sessions to execute models concurrently without interference.

Model Output Customization

For RK3588 YOLOv5 model:
- When converting to ONNX, remove the cat operation in the forward layer.
- Configure the model to output three feature maps: 20x20, 40x40, and 80x80.
- Modify the necessary files: yolo.py and export.py.

Performance Overview

Resource Usage:
- One model uses:
  - 1.2T of the NPU.
  - 40% of the CPU for pre-processing, inference, and post-processing (including frame drawing).
Inference Time:
- Achieves an inference time of 20ms.
CPU Monitoring:
- Use the perf top -p command to view the CPU usage rate, down to specific functions.

Quick Start

Compilation

Run the following command to build the project:

bash build_rk3588_yolov5.sh

Testing

Run the following command to test the deployment:

bash test_rk3588_yolov5.sh

Quickrun Deployment

Modify the following parameters in the code as per your project’s requirements:

#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NUMB_MAX_SIZE 64 #define OBJ_CLASS_NUM 1 #define NMS_THRESH 0.25 #define BOX_THRESH 0.5

Robot Demo Video

View Robot Video on GitHub

机器人视频观看 You can also watch the video below:

Your browser does not support the video tag.

License

Quickrun is licensed under the MIT License by Jim.

Quickrun

Quickrun is a software designed for the efficient and high-concurrency deployment of multiple models on the RK3588 platform with RKNN.

Software Framework

Session-Based Design:
- Implements a session concept, allowing the definition of multiple sessions for different task requirements.
- Example: Supports scenarios like charging pile detection, garbage classification, and cliff detection where multiple models share the same camera, e.g., using YOLOv5.
Message Queue for Data Management:
- Uses a message queue to store photo data, preventing frame loss and ensuring efficient concurrency.
- Photo data is collected at 25fps, with a total processing time (pre-processing, inference, post-processing) of 40ms per frame.
Input Processing:
- Handles model input of 640x640 while the camera input is 640x480.
- Decodes the input using cv::imdecode, converts it to RGB format, and leverages RGA for accelerated proportional scaling.
Threaded Model Execution:
- Three independent threads for three sessions to execute models concurrently without interference.

Model Output Customization

For RK3588 YOLOv5 model:
- When converting to ONNX, remove the cat operation in the forward layer.
- Configure the model to output three feature maps: 20x20, 40x40, and 80x80.
- Modify the necessary files: yolo.py and export.py.

Performance Overview

Resource Usage:
- One model uses:
  - 1.2T of the NPU.
  - 40% of the CPU for pre-processing, inference, and post-processing (including frame drawing).
Inference Time:
- Achieves an inference time of 20ms.
CPU Monitoring:
- Use the perf top -p command to view the CPU usage rate, down to specific functions.

Quick Start

Compilation

Run the following command to build the project:

bash build_rk3588_yolov5.sh

Testing

Run the following command to test the deployment:

bash test_rk3588_yolov5.sh

Quickrun Deployment

Modify the following parameters in the code as per your project’s requirements:

#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NUMB_MAX_SIZE 64 #define OBJ_CLASS_NUM 1 #define NMS_THRESH 0.25 #define BOX_THRESH 0.5

Robot Demo Video

View Robot Video on GitHub

机器人视频观看 You can also watch the video below:

Your browser does not support the video tag.

License

Quickrun is licensed under the MIT License by Jim.

Quickrun

Quickrun is a software designed for the efficient and high-concurrency deployment of multiple models on the RK3588 platform with RKNN.

Software Framework

Session-Based Design:
- Implements a session concept, allowing the definition of multiple sessions for different task requirements.
- Example: Supports scenarios like charging pile detection, garbage classification, and cliff detection where multiple models share the same camera, e.g., using YOLOv5.
Message Queue for Data Management:
- Uses a message queue to store photo data, preventing frame loss and ensuring efficient concurrency.
- Photo data is collected at 25fps, with a total processing time (pre-processing, inference, post-processing) of 40ms per frame.
Input Processing:
- Handles model input of 640x640 while the camera input is 640x480.
- Decodes the input using cv::imdecode, converts it to RGB format, and leverages RGA for accelerated proportional scaling.
Threaded Model Execution:
- Three independent threads for three sessions to execute models concurrently without interference.

Model Output Customization

For RK3588 YOLOv5 model:
- When converting to ONNX, remove the cat operation in the forward layer.
- Configure the model to output three feature maps: 20x20, 40x40, and 80x80.
- Modify the necessary files: yolo.py and export.py.

Performance Overview

Resource Usage:
- One model uses:
  - 1.2T of the NPU.
  - 40% of the CPU for pre-processing, inference, and post-processing (including frame drawing).
Inference Time:
- Achieves an inference time of 20ms.
CPU Monitoring:
- Use the perf top -p command to view the CPU usage rate, down to specific functions.

Quick Start

Compilation

Run the following command to build the project:

bash build_rk3588_yolov5.sh

Testing

Run the following command to test the deployment:

bash test_rk3588_yolov5.sh

Quickrun Deployment

Modify the following parameters in the code as per your project’s requirements:

#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NUMB_MAX_SIZE 64 #define OBJ_CLASS_NUM 1 #define NMS_THRESH 0.25 #define BOX_THRESH 0.5

Robot Demo Video

View Robot Video on GitHub

机器人视频观看 You can also watch the video below:

Your browser does not support the video tag.

License

Quickrun is licensed under the MIT License by Jim.

Quickrun

Quickrun is a software designed for the efficient and high-concurrency deployment of multiple models on the RK3588 platform with RKNN.

Software Framework

Session-Based Design:
- Implements a session concept, allowing the definition of multiple sessions for different task requirements.
- Example: Supports scenarios like charging pile detection, garbage classification, and cliff detection where multiple models share the same camera, e.g., using YOLOv5.
Message Queue for Data Management:
- Uses a message queue to store photo data, preventing frame loss and ensuring efficient concurrency.
- Photo data is collected at 25fps, with a total processing time (pre-processing, inference, post-processing) of 40ms per frame.
Input Processing:
- Handles model input of 640x640 while the camera input is 640x480.
- Decodes the input using cv::imdecode, converts it to RGB format, and leverages RGA for accelerated proportional scaling.
Threaded Model Execution:
- Three independent threads for three sessions to execute models concurrently without interference.

Model Output Customization

For RK3588 YOLOv5 model:
- When converting to ONNX, remove the cat operation in the forward layer.
- Configure the model to output three feature maps: 20x20, 40x40, and 80x80.
- Modify the necessary files: yolo.py and export.py.

Performance Overview

Resource Usage:
- One model uses:
  - 1.2T of the NPU.
  - 40% of the CPU for pre-processing, inference, and post-processing (including frame drawing).
Inference Time:
- Achieves an inference time of 20ms.
CPU Monitoring:
- Use the perf top -p command to view the CPU usage rate, down to specific functions.

Quick Start

Compilation

Run the following command to build the project:

bash build_rk3588_yolov5.sh

Testing

Run the following command to test the deployment:

bash test_rk3588_yolov5.sh

Quickrun Deployment

Modify the following parameters in the code as per your project’s requirements:

#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NUMB_MAX_SIZE 64 #define OBJ_CLASS_NUM 1 #define NMS_THRESH 0.25 #define BOX_THRESH 0.5

Robot Demo Video

View Robot Video on GitHub

机器人视频观看 You can also watch the video below:

Your browser does not support the video tag.

License

Quickrun is licensed under the MIT License by Jim.

Quickrun

Quickrun is a software designed for the efficient and high-concurrency deployment of multiple models on the RK3588 platform with RKNN.

Software Framework

Session-Based Design:
- Implements a session concept, allowing the definition of multiple sessions for different task requirements.
- Example: Supports scenarios like charging pile detection, garbage classification, and cliff detection where multiple models share the same camera, e.g., using YOLOv5.
Message Queue for Data Management:
- Uses a message queue to store photo data, preventing frame loss and ensuring efficient concurrency.
- Photo data is collected at 25fps, with a total processing time (pre-processing, inference, post-processing) of 40ms per frame.
Input Processing:
- Handles model input of 640x640 while the camera input is 640x480.
- Decodes the input using cv::imdecode, converts it to RGB format, and leverages RGA for accelerated proportional scaling.
Threaded Model Execution:
- Three independent threads for three sessions to execute models concurrently without interference.

Model Output Customization

For RK3588 YOLOv5 model:
- When converting to ONNX, remove the cat operation in the forward layer.
- Configure the model to output three feature maps: 20x20, 40x40, and 80x80.
- Modify the necessary files: yolo.py and export.py.

Performance Overview

Resource Usage:
- One model uses:
  - 1.2T of the NPU.
  - 40% of the CPU for pre-processing, inference, and post-processing (including frame drawing).
Inference Time:
- Achieves an inference time of 20ms.
CPU Monitoring:
- Use the perf top -p command to view the CPU usage rate, down to specific functions.

Quick Start

Compilation

Run the following command to build the project:

bash build_rk3588_yolov5.sh

Testing

Run the following command to test the deployment:

bash test_rk3588_yolov5.sh

Quickrun Deployment

Modify the following parameters in the code as per your project’s requirements:

#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NUMB_MAX_SIZE 64 #define OBJ_CLASS_NUM 1 #define NMS_THRESH 0.25 #define BOX_THRESH 0.5

Robot Demo Video

View Robot Video on GitHub

机器人视频观看 You can also watch the video below:

Your browser does not support the video tag.

License

Quickrun is licensed under the MIT License by Jim.

Quickrun

Quickrun is a software designed for the efficient and high-concurrency deployment of multiple models on the RK3588 platform with RKNN.

Software Framework

Session-Based Design:
- Implements a session concept, allowing the definition of multiple sessions for different task requirements.
- Example: Supports scenarios like charging pile detection, garbage classification, and cliff detection where multiple models share the same camera, e.g., using YOLOv5.
Message Queue for Data Management:
- Uses a message queue to store photo data, preventing frame loss and ensuring efficient concurrency.
- Photo data is collected at 25fps, with a total processing time (pre-processing, inference, post-processing) of 40ms per frame.
Input Processing:
- Handles model input of 640x640 while the camera input is 640x480.
- Decodes the input using cv::imdecode, converts it to RGB format, and leverages RGA for accelerated proportional scaling.
Threaded Model Execution:
- Three independent threads for three sessions to execute models concurrently without interference.

Model Output Customization

For RK3588 YOLOv5 model:
- When converting to ONNX, remove the cat operation in the forward layer.
- Configure the model to output three feature maps: 20x20, 40x40, and 80x80.
- Modify the necessary files: yolo.py and export.py.

Performance Overview

Resource Usage:
- One model uses:
  - 1.2T of the NPU.
  - 40% of the CPU for pre-processing, inference, and post-processing (including frame drawing).
Inference Time:
- Achieves an inference time of 20ms.
CPU Monitoring:
- Use the perf top -p command to view the CPU usage rate, down to specific functions.

Quick Start

Compilation

Run the following command to build the project:

bash build_rk3588_yolov5.sh

Testing

Run the following command to test the deployment:

bash test_rk3588_yolov5.sh

Quickrun Deployment

Modify the following parameters in the code as per your project’s requirements:

#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NUMB_MAX_SIZE 64 #define OBJ_CLASS_NUM 1 #define NMS_THRESH 0.25 #define BOX_THRESH 0.5

Robot Demo Video

View Robot Video on GitHub

机器人视频观看 You can also watch the video below:

Your browser does not support the video tag.

License

Quickrun is licensed under the MIT License by Jim.

Quickrun

Quickrun is a software designed for the efficient and high-concurrency deployment of multiple models on the RK3588 platform with RKNN.

Software Framework

Session-Based Design:
- Implements a session concept, allowing the definition of multiple sessions for different task requirements.
- Example: Supports scenarios like charging pile detection, garbage classification, and cliff detection where multiple models share the same camera, e.g., using YOLOv5.
Message Queue for Data Management:
- Uses a message queue to store photo data, preventing frame loss and ensuring efficient concurrency.
- Photo data is collected at 25fps, with a total processing time (pre-processing, inference, post-processing) of 40ms per frame.
Input Processing:
- Handles model input of 640x640 while the camera input is 640x480.
- Decodes the input using cv::imdecode, converts it to RGB format, and leverages RGA for accelerated proportional scaling.
Threaded Model Execution:
- Three independent threads for three sessions to execute models concurrently without interference.

Model Output Customization

For RK3588 YOLOv5 model:
- When converting to ONNX, remove the cat operation in the forward layer.
- Configure the model to output three feature maps: 20x20, 40x40, and 80x80.
- Modify the necessary files: yolo.py and export.py.

Performance Overview

Resource Usage:
- One model uses:
  - 1.2T of the NPU.
  - 40% of the CPU for pre-processing, inference, and post-processing (including frame drawing).
Inference Time:
- Achieves an inference time of 20ms.
CPU Monitoring:
- Use the perf top -p command to view the CPU usage rate, down to specific functions.

Quick Start

Compilation

Run the following command to build the project:

bash build_rk3588_yolov5.sh

Testing

Run the following command to test the deployment:

bash test_rk3588_yolov5.sh

Quickrun Deployment

Modify the following parameters in the code as per your project’s requirements:

#define OBJ_NAME_MAX_SIZE 16 #define OBJ_NUMB_MAX_SIZE 64 #define OBJ_CLASS_NUM 1 #define NMS_THRESH 0.25 #define BOX_THRESH 0.5

Robot Demo Video

View Robot Video on GitHub

机器人视频观看 You can also watch the video below:

Your browser does not support the video tag.

License

Quickrun is licensed under the MIT License by Jim.

Name		Name	Last commit message	Last commit date
3rdparty		3rdparty
assets		assets
getusbdevice		getusbdevice
kernel		kernel
libv4l2		libv4l2
mpp/encode		mpp/encode
queue		queue
video		video
.gitignore		.gitignore
CMakeLists.txt		CMakeLists.txt
README.md		README.md
build_rk3588_yolov5.sh		build_rk3588_yolov5.sh
main.cc		main.cc
test_rk3588_yolov5.sh		test_rk3588_yolov5.sh

Name		Name	Last commit message	Last commit date
3rdparty		3rdparty
assets		assets
getusbdevice		getusbdevice
kernel		kernel
libv4l2		libv4l2
mpp/encode		mpp/encode
queue		queue
video		video
.gitignore		.gitignore
CMakeLists.txt		CMakeLists.txt
README.md		README.md
build_rk3588_yolov5.sh		build_rk3588_yolov5.sh
main.cc		main.cc
test_rk3588_yolov5.sh		test_rk3588_yolov5.sh

Name		Name	Last commit message	Last commit date
3rdparty		3rdparty
assets		assets
getusbdevice		getusbdevice
kernel		kernel
libv4l2		libv4l2
mpp/encode		mpp/encode
queue		queue
video		video
.gitignore		.gitignore
CMakeLists.txt		CMakeLists.txt
README.md		README.md
build_rk3588_yolov5.sh		build_rk3588_yolov5.sh
main.cc		main.cc
test_rk3588_yolov5.sh		test_rk3588_yolov5.sh

Name		Name	Last commit message	Last commit date
3rdparty		3rdparty
assets		assets
getusbdevice		getusbdevice
kernel		kernel
libv4l2		libv4l2
mpp/encode		mpp/encode
queue		queue
video		video
.gitignore		.gitignore
CMakeLists.txt		CMakeLists.txt
README.md		README.md
build_rk3588_yolov5.sh		build_rk3588_yolov5.sh
main.cc		main.cc
test_rk3588_yolov5.sh		test_rk3588_yolov5.sh

Name		Name	Last commit message	Last commit date
3rdparty		3rdparty
assets		assets
getusbdevice		getusbdevice
kernel		kernel
libv4l2		libv4l2
mpp/encode		mpp/encode
queue		queue
video		video
.gitignore		.gitignore
CMakeLists.txt		CMakeLists.txt
README.md		README.md
build_rk3588_yolov5.sh		build_rk3588_yolov5.sh
main.cc		main.cc
test_rk3588_yolov5.sh		test_rk3588_yolov5.sh

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Quickrun

Software Framework

Model Output Customization

Performance Overview

Quick Start

Compilation

Quickrun Deployment

Robot Demo Video

License

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Quickrun

Software Framework

Model Output Customization

Performance Overview

Quick Start

Compilation

Quickrun Deployment

Robot Demo Video

License

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

Quickrun

Software Framework

Model Output Customization

Performance Overview

Quick Start

Compilation

Quickrun Deployment

Robot Demo Video

License

About

Topics

Resources

Stars

Watchers

Forks

Releases

Packages

Used by

Contributors

Languages

Latest commit

History

Folders and files

Repository files navigation

Quickrun

Software Framework

Model Output Customization

Performance Overview