Data collection and annotation are often tedious and time-consuming to achieve satisfactory results in image classification. The transfer learning technique enables to overcome this challenge by reducing the number of images and the training time needed to add a new class. It is applied here to flower classification, but it can be extended to many other use cases.
– The tutorial presents how to use a technique called “Transfer learning” to quickly train a deep learning model to classify images.
– The tutorial is based on the computer vision function pack FP-AI-VISION1
Vision: Camera module bundle (reference: B-CAMS-OMV)
Dataset: Dataset for flower recognition (License CC BY 2.0)
5 classes of flowers: daisy, dandelion, rose, sunflower, tulip
RGB color images
Model: MobileNetV2 alpha 0.35
Input size: 128x128x3
407 KB Flash for weights
225 KB RAM for activations
Float model: 87%
Quantized model: 88%
Performance on STM32H747 (High-perf) @ 400 MHz
Inference time: 112 ms
Frame rate: 8.9 fps