Improving performance for segmenting images
In this recipe, we will apply transfer learning and fine-tuning to semantic segmentation, a CV task.
In the fourth recipe, Segmenting objects in images with MXNet: PSPNet and DeepLab-v3, in Chapter 5, Analyzing Images with Computer Vision, we saw how we could use GluonCV to retrieve pre-trained models and use them directly for a semantic segmentation task, effectively leveraging past knowledge by using the architecture and the weights/parameters of the pre-trained model.
In this recipe, we will continue leveraging the weights/parameters of the model, obtained for a task consisting of classifying images among a set of 21 classes using semantic segmentation models. The dataset used for the pre-training was MS COCO (source task) and we will run several experiments to evaluate our models in a new (target) task, using the Penn-Fudan Pedestrian dataset. In these experiments, we will also include knowledge from the target dataset to improve...
