Integrating CNN Models with Flutter Using TensorFlow Lite
summary
Offline gesture classifier built with Flutter and TensorFlow Lite that runs on-device and sometimes misclassifies but generally works; model is MobileNetV3 fine-tuned on the HAGRID hand-gesture dataset (19 classes), trained for 20 epochs (~8 hours) and hitting ~98% accuracy; comparison demo shows an MLP (6,442 params, 82%) vs CNN (same param budget, 95%); Flutter integration includes resize to 300×300, normalize, channel reorder, batch dim, and running inference in a separate isolate.
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chapters (9)
- 0:00Flutter and AI integration
- 1:27Supervised learning basics
- 7:28CNNs outperform MLPs
- 16:08Training and evaluating models
- 20:44Gradient descent optimization
- 23:38Fine-tuning pre-trained CNNs
- 26:25TensorFlow Lite with Flutter
- 28:54Debugging Flutter ML
- 30:18Beyond rock paper scissors
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