I need to build a plant identification app that works without an internet connection. Can I run a custom .tflite model directly on the device using Flutter? What are the best packages for handling image pre-processing before passing the data to the interpreter, and does it support GPU acceleration on iOS?
3 answers
Yes, you can absolutely run models offline using the tflite_flutter package. For image pre-processing, most developers use the image package to resize and normalize the camera stream bytes into the specific input shape your model requires (e.g., 224x224). Regarding acceleration, tflite_flutter supports "Delegates." On iOS, you can use the CoreML delegate, and on Android, the GPU or NNAPI delegates. This offloads the heavy math from the CPU, which not only speeds up the identification process but also significantly reduces battery drain during long sessions.
Does the tflite_flutter package work well with the camera package's image stream? I'm worried about the conversion overhead between CameraImage and the model input format.
For a simpler setup, I suggest looking into Google ML Kit. It has a built-in "Image Labeling" API that is already optimized for Flutter and requires zero model training.
Good suggestion, Laura. ML Kit is perfect if you don't have a custom model and just need to recognize general objects or text quickly.
Jeremy, the conversion can be a bottleneck. The key is to use "Isolates" in Dart. By offloading the image transformation (YUV to RGB) and the inference to a background Isolate, you prevent the UI thread from janking. You can pass the byte buffer to the Isolate, perform the math, and send only the result (like 'Hibiscus - 98%') back to the main thread. This ensures your camera preview stays at a consistent 60 FPS while the AI works hard in the background.