Tipper predicts if a pitch will be in or out of the strike zone in real time. The batter will see a green or red light illuminate in their peripheral vision if the pitch will be in or out of the strike zone, respectively.
How It Works
A modified Nerf tennis ball launcher is programmatically fired with a solenoid. A 100FPS camera is pointed in the direction of the launcher and captures two successive images of the ball early in flight.
A convolutional neural network running on an NVIDIA Jetson AGX Xavier rapidly classifies these images against a model that was built during the training phase of the project. If the images are classified as in the strike zone, a green LED on a pair of glasses (in the wearer’s peripheral vision) is lit. Conversely, if the ball is predicted to be out of the strike zone, a red LED is lit.
Noticed in the GitHub it mentioned speeding up the processing - you could use TensorRT to optimize the inferencing. Since you are using PyTorch today, you could either export your model to ONNX and load it with the TensorRT Python API, or use the torch2trt tool. You should get a nice speed-up from it.