Model quantization is a technique used to reduce the precision of the numerical representations in a neural network. Typically, neural network models use 32-bit floating-point numbers to represent weights and activations. Quantization involves converting these high-precision numbers to lower-precision formats, such as 8-bit integers or 16-bit floating-point numbers.
Static Quantized model provided faster inference speed with around 25% more FPS than the original Yolo model. This too with similar kind of Confidence level.
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Yolov8n | Static Quantized Yolov8n |
- Clone the repository:
git clone https://github.com/majipa007/Quantization-YOLOv8.git
- Install requirements.txt
pip install -r requirements.txt
Then you are good to go.