Mut1ny’s fast people detector is a deep neural network human head detector that has remarkable fast detection speed. Our people detector can cover the full hemisphere of people’s poses towards the camera. It can be used in diverse environments that can range between a few people or a semi-crowded scenario.
The detectors network architecture is based on Version five in the popular YOLO network architecture family. The network has been trained on our unique head dataset that covers a wide range of scenarios, different ethnicity, gender and age .
This product is the ideal companion in combination with our other facial processing networks, especially if the head only takes up a smaller percentage of the image/frame. Or if there are multiple people in the footage.
The model is deliverable in these supported neural network formats
But if you need any other neural network format just get in contact with us.
What is included in the fast people detector deep neural network?
- The neural network model in the formats above
- For PyTorch format only:
- Python inference code using our model
- C++ inference code using our model
- For ONNX format only:
- C# inference code using our model using ONNXRuntime
- C++ inference code using our model using ONNXRuntime
Why buy a deep neural network model? When you can build and train one yourself? Or get one free from the internet?
- Training a segmentation model from scratch using a large enough dataset takes between 2-3 days even with a high-end equipped GPU. This translates to € 80-150,- public cloud instance spending costs alone.
- There is no need to spend any development time, but instead you can fully concentrate on your product feature.
- Acquiring a large enough annotated dataset can take man-months.
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