Transfer learning applied to flower recognition 

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. 

Approach

• The STM32 model zoo provides everything you need to train and retrain on your own data
• The solution uses a technique called “transfer learning” to quickly train a deep learning model to classify images
• The model can be easily deployed on the STM32H747 discovery kit with the STM32 model zoo Python scripts
• The use case presented is based on flower recognition.

Sensor

Vision: Camera module bundle (reference: B-CAMS-OMV)