We are using YoloV5x model for object detection. In out setup we have Geforce GT730 GPU and we have build the torch(V1.10) and torchvision (V0.11) from source. The model is able to detect the CUDA and is working. When we check the model prediction time, the time taken from the prediction is high.
In our application we will be using 4 cameras for object detection. When we tested the YoloV5 model with 2 cameras, the time taken is approximately 1.6-1.8 sec with imgsz size of 640.
After training the model we have saved the model with .pt extension.
Is there any way where we can increase the model prediction speed? Also the CPU usage is 100% when we run the model with 2 cameras.