Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
YOLOv8n is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
Original model:
Re-implenented by:
Model type: Computer Vision
License:
Resources for more information:
Training Details
The model was trained on consisting of roughly 21 000 training images with one single "License plate" class.
The images include cars, trucks and motorcycles from multiple countries as to help with generalization.
Testing Details
Testing was performed on a test split of 1018 images from the same dataset. The below metrics are based on the underlying source model before it was converted to RVC2 and RVC4 compatible format.
Metrics
Class
Images
Instances
Precision
Recall
AP@50
AP@50-95
License Plate
1218
1048
0.831
0.96
0.895
0.637
Technical Specifications
Input/Output Details
Input:
Name: images
Info: NCHW BGR non-normalized image
Output:
Name: output1_yolov6r2
Info: Unprocessed output of the first channel
Name: output2_yolov6r2
Info: Unprocessed output of the second channel
Name: output3_yolov6r2
Info: Unprocessed output of the third channel
Model Architecture
Please consult the for more information on model architecture.
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Utilization
Models converted for RVC Platforms can be used for inference on OAK devices.
DepthAI pipelines are used to define the information flow linking the device, inference model, and the output parser (as defined in model head(s)).
Below, we present the most crucial utilization steps for the particular model.
Please consult the docs for more information.
The model is automatically parsed by DAI and it outputs the message (bounding boxes, labels, and scores of the detected licence plates).
Get model output(s):
while pipeline.isRuning():
nn_output: dai.ImgDetections = parser_output_queue.get()
Example
You can quickly run the model using our example.
The example demonstrates how to build a 3-stage DepthAI pipeline consisting of a car detection model, a licence plate detection model, and a text recognition model.
It automatically downloads the models, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
To try it out, run:
python3 main.py
Pretrained YOLOv8 model for detecting vehicle license plates
