科研成果详情

In recent years, deep learning has been widely used in various computer vision tasks. Because the task of solving vegetable pictures are different in the local critical areas, and solving the classification of vegetable categories to meet the needs of users has become an urgent problem to be solved. In this paper, we propose fine-grained image recognition based on Vegetable Dataset, and uses multi-scale iteration to extract critical area characteristics, which the learning at each scale consists of a classification subnetwork and the critical area. In addition, the multi-scale neural network is optimized by two loss functions, to learn accurate critical area and fine-grained feature. Finally, we further prove its scalability and effectiveness in comparing different datasets and different training methods, we get satisfactory results on Vegetable Dataset.