Record
Version | Tag | Date | Note |
Gamma release | gamma_ver | 2024/07/19 | The 3rd formal release |
Beta release | beta_ver | 2024/07/11 | The 2nd formal release |
Alpha release | alpha_ver | 2024/06/11 | The 1st formal release |
Release note
Gamma release
Add some NN modules and Samples.
a. Yolov8 Pose for human posture detection.
b. Yolov8 OBB (oriented bounding box), can detect the peripheral direction of objects.
c. Yolov8 Segmentation. The detected targets can be identified using masks.
d. Yolov8 Classification. Each target in the detected image can be classified based on confidence level.
e. Vehicle attributes. Can identify trucks, cars, buses, and their respective colors.
f. License plate recognition. It can detect license plates and recognize their contents.
Parallel operations in multi-model applications can maximize the utilization of NPU and CPU, ultimately maximizing the processing frame rate of the application.
Implement official and customized partitions.
Arrangement of some folder struct such as tool and algo and so on.
The parameters of nnf_sample are designed in the standard Linux option (-, --) way.
Beta release
Open source the NN Framework to facilitate users to better understand the internal operations of NNF. In the next version, we will distinguish between official and customized partitions, making it easier for users to add their own models or copy an official model to a customized partition for customized modifications. In future versions, we will consider providing a more user-friendly and convenient way to port the code generated by ACUITY TOOL to NNF.
Optimize the pipeline dataflow. Optimized some link matching and data flow transmission processes in the pipeline flow, making the data flow path more convenient and the system running smoother.
Optimize model plugin. Crop the directory structure related to the model, merge the content related to the model, and make the structure more compact and clear.
Add some NN modules.
Optical character recognition.
In the OCR section, we optimized the optical processing, tilt processing, and flip detection of the collected images, greatly improving the accuracy of character recognition.
Age recognition.
Classify the identified individuals into 8 age groups.
Face det and Age recognition.
Combining facial detection module to classify people into 8 age groups.
Yolov8 Detection.
Mainly used for object detection tasks, it can quickly detect 80 different targets and achieve fast and accurate image analysis.
Add command for model list. You can list the currently supported models and serial connection methods of the system through commands. In future versions, model related information will be dynamically listed based on the actual situation inside the code.
Regarding the user interface, maintain compatibility and consistency with the alpha version.
Adjust the internal structure of NNF and output the initial version of open-source code.
Optimize the system to make it more robust, and after long-term testing, the system is stable.
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