I'm working on a Flutter app that needs to perform real-time image classification on-device. I want to avoid high latency from API calls. Has anyone successfully integrated TensorFlow Lite models (.tflite) for edge computing in Flutter? What is the performance like on mid-range Android devices?
3 answers
I recently finished a project using the tflite_flutter package, and it works remarkably well. The key is to offload the inference to a separate Isolate to prevent the UI thread from freezing. On a mid-range device like a Galaxy A54, we achieved around 15-20 FPS for live camera feed classification. You should also look into using the GPU delegate to speed up calculations. Just make sure your model is quantized to 8-bit integers to keep the app size small and the execution speed fast without sacrificing too much accuracy.
Have you compared the performance of the generic TFLite package against Google's ML Kit for standard tasks like face detection or OCR?
Make sure to use the CameraImage stream correctly. Converting YUV420 to RGB can be a bottleneck if you do it in pure Dart code.
Great point! Using C++ via FFI for the image conversion is a game-changer for maintaining a high frame rate in AI-driven apps.
Ralph, ML Kit is much easier to set up but less flexible. If you need a custom-trained model for something niche, TFLite is the only way to go. For basic stuff, ML Kit is definitely faster to implement.