I am currently working on a computer vision project that needs to run on low-power edge devices, but the inference time for our deep learning model is way too high. We are using TensorFlow, but the lag is making the real-time detection unusable. Are there specific quantization techniques or model pruning methods that you’ve found effective without losing too much accuracy for object detection?
3 answers
You should definitely look into TensorFlow Lite for your deployment. Converting your model to a TFLite format allows you to use post-training quantization, which can compress your weights from 32-bit floats to 8-bit integers. This usually results in a 4x reduction in model size and a significant speedup on mobile and edge hardware. In my last project, we also used 'Knowledge Distillation' where a smaller 'student' model learns to mimic a larger 'teacher' model, which helped us maintain about 95% accuracy while cutting latency in half.
What specific hardware are you targeting for the edge deployment? The optimization strategy for an NVIDIA Jetson is very different from a generic ARM-based MCU.
Try switching your backbone to a more efficient architecture like MobileNetV3 or Tiny-YOLO. They are designed specifically for the constraints of edge devices.
Susan is right. Sometimes we get too attached to complex models like ResNet when a lighter architecture is perfectly fine for the specific use case. Swapping the backbone is often much more effective than trying to prune a model that was never meant for the edge in the first place.
Kevin, we are actually targeting a Raspberry Pi 4 for the prototype but might move to a specialized TPU later. Right now, the bottleneck seems to be the CPU handling the pre-processing of the image frames before they even hit the model. I’m wondering if there is a way to offload that or if I should just simplify the input resolution to save on the initial computation costs.