In this paper, we present two image classification models on the Tiny ImageNet dataset. We built two very different networks from scratch based on the idea of Densely Connected Convolution Networks. The architecture of the networks is designed based on the image resolution of this specific dataset and by calculating the Receptive Field of the convolution layers. We also used some non-conventional techniques related to image augmentation and Cyclical Learning Rate to improve the accuracy of our models. The networks are trained under high constraints and low computation resources. We aimed to achieve top-1 validation accuracy of 60%; the results and error analysis are also presented.

翻译：在本文中,我们在“小图像网络”数据集上展示了两个图像分类模型。我们根据“连通性强的革命网络”的设想,从零开始建立了两个截然不同的网络。网络结构的设计基于这一特定数据集的图像分辨率和通过计算革命层的受体领域。我们还使用了一些与图像增强和周期学习率有关的非常规技术来提高模型的准确性。这些网络在高度制约和低计算资源下接受培训。我们的目标是达到60%的顶层一级验证准确性;结果和误差分析也作了介绍。