In rural agricultural areas, relying on cloud API round-trips to OpenAI or Google Cloud Vision is a non-starter due to spotty 2G/3G connectivity. When building FasalSathii, we needed on-device leaf disease diagnosis that worked completely offline in the browser.
1. Quantizing Weights with INT8 Post-Training Quantization
Our initial MobileNetV3 model weighed 142MB in float32. By using TensorFlow Lite's post-training integer quantization with representative calibration datasets, we compressed the model down to 8.2MB with only a 0.7% drop in top-1 classification accuracy.
# Python TFLite Converter
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = representative_data_gen
tflite_quant_model = converter.convert()KEY TAKEAWAY:Quantization is mandatory when deploying neural networks into browser memory limits.
TAGS:#TensorFlow Lite#PWA#WASM#Next.js 16