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Model Details
Model Description
QRDet is a YOLOv8 based convolutional neural network model designed for QR Code detection. It is highly effective and accurate even for more tricky images.
We implement here the nano version of the model.
Developed by: Eric Cañas
Shared by:
Model type: Computer Vision
License:
Resources for more information:
Photo by Erik Mclean from
Training Details
Training Data
No data available.
Testing Details
Metrics
No data available.
Technical Specifications
Input:
Name: images
Info: BGR image
Output1:
Name: output1_yolov6r2
Info: Detection output 1
Output2:
Name: output2_yolov6r2
Info: Detection output 2
Output3:
Name: output3_yolov6r2
Info: Detection output 3
Model Architecture
Backbone: CSPDarknet53
Neck: Path Aggregation Network (PANet)
Head: Anchor-free object detection head (pruned of concatenation)
* Benchmarked with , using 2 threads (and the DSP runtime in balanced mode for RVC4).
* Parameters and FLOPs are obtained from the package.
Quantization
RVC4 version of the model was quantized using a custom dataset.
This was created by taking a 50-image subset of the dataset made publicli available over Roboflow by Capstone Project.
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 and scores of the detected QR codes).
Get model output(s):
while pipeline.isRuning():
nn_output: dai.ImgDetections = parser_output_queue.get()
Example
You can quickly run the model using our script.
It automatically downloads the model, creates a DepthAI pipeline, runs the inference, and displays the results using our DepthAI visualizer tool.
To try it out, run:
