Our new model ZOO works with DepthAI V3. Find out more in our documentation.
Model Details
Model Description
SCRFD was originally developed and trained for face detection tasks but authors also trained it on person detection tasks as it turns out it works pretty well. You can read more about the SCRFD model in a SCRFD model card. Overall it is a lightweight, fast, and accurate person detector. It outputs bounding boxes with appropriate scores and 5 keypoints - the center and corners of the bounding box.
Developed by: InsightFace
Shared by:
Model type: Computer Vision
License:
Resources for more information:
Training Details
Training Data
The model was trained on . It is split into training, validation, and test sets. WiderPerson contains a total of 13,382 images with 399,786 annotations, i.e., 29.87 annotations per image, which means this dataset contains dense pedestrians with various kinds of occlusions.
Testing Details
Metrics
Results for person detection task are not given so we are listing the evaluation results for the same architecture for face detection task.
Evaluation of the model on WIDERFace dataset for all three categories: Easy, Medium and Hard. Results are taken from .
Category
mAP
Easy
93.78
Medium
92.16
Hard
77.87
Technical Specifications
Input/Output Details
Input:
Name: input
Info: NCHW BGR un-normalized image
Output:
Name: multiple (see NN archive)
Info: Classification scores, bounding boxes, and keypoints for a multitude of detections.
* 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 50-image subset of dataset.
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.
SCRFDParser that outputs message (bounding boxes and confidence scores for every detected person).
Get parsed output(s):
while pipeline.isRuning():
parser_output: ImgDetectionsExtended = 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:
