Читать книгу Machine Learning Algorithms and Applications - Группа авторов - Страница 36

In comparison with the conventional method of extracting egg count information using digital images that hardly require any training data, the proposed method that employs the CNN technique required large datasets to learn the features automatically to provide the required results. The CNN method uses plenty of training data along with test and validation datasets as the number of hidden layers increases.

There are many datasets available for free that can be downloaded to train our own CNN models to classify handwritten digits, identify objects, and many more. But there is no single public dataset available corresponding with the sericulture field especially silkworm egg counting or classification. So, in our work, training datasets were generated by cropping class images from the silkworm egg sheet and providing class labels and other features that are necessary for CNN training such as egg center location. Over 400K image set was generated for egg location and FB class and over 100K image set for individual classes (HC and UHC). Also, data augmentation is implemented to increase the datasets.