YOLOv5-Lite performs ablation experiments on YOLOv5 to reduce model size, FLOPs, memory usage, and parameters while maintaining detection accuracy. It removes the Focus layer and slice operations for easier deployment and adds shuffle channels for faster inference. The model supports deployment across TensorRT, TFLite, NCNN, MNN, ONNXRuntime, and other inference runtimes, achieving 10+ FPS on Raspberry Pi 4B with 320x320 input.