I have designed a high-accuracy CNN using Keras, but I am struggling with the conversion to TensorFlow Lite. The model is too large for my Android app, and I am seeing significant latency. Should I prioritize post-training quantization to reduce the size, or is it better to use pruning during the training phase to ensure I don’t lose too much accuracy for mobile users?
3 answers
For most mobile-centric TensorFlow projects, post-training integer quantization is the "low-hanging fruit" for optimization. It can typically shrink your model by 4x with minimal accuracy loss. However, if your CNN uses very specific custom layers, make sure they are supported by the TFLite interpreter. I’ve found that using the "float16" quantization is a great middle ground if you are deploying to newer smartphones with GPU delegates. If the latency is still too high, you might want to look into the Model Optimization Toolkit for selective pruning, which removes the least important weights before you even start the conversion process.
Are you using the standard TFLite converter, or have you tried the experimental JAX backend for your TensorFlow Lite exports yet?
I’d suggest using the TensorFlow Model Analyzer tool first. It will show you exactly which layers are the bottleneck for your mobile inference speed.
I totally agree with Megan! Profiling the model on the actual hardware is the only way to be sure that your TensorFlow optimizations are actually working as intended.
That is a great point, Justin! I have been sticking to the standard Python API for the conversion. I didn't realize the JAX backend was stable enough for production yet. Does it handle the metadata generation automatically, or do I still need to manually map the input and output tensors? I'm trying to automate as much of the pipeline as possible so our mobile devs can just pull the latest flatbuffer without me having to write a custom wrapper every single time we update the model.