ResNets (or Residual Networks) are one of the most commonly used models for image classification tasks. In this project, we design and train a modified ResNet model for CIFAR-10 image classification. In particular, we aimed at maximizing the test accuracy on the CIFAR-10 benchmark while keeping the size of our ResNet model under the specified fixed budget of 5 million trainable parameters. Model size, typically measured as the number of trainable parameters, is important when models need to be stored on devices with limited storage capacity (e.g. IoT/edge devices). In this article, we present our residual network design which has less than 5 million parameters. We show that our ResNet achieves a test accuracy of 96.04% on CIFAR-10 which is much higher than ResNet18 (which has greater than 11 million trainable parameters) when equipped with a number of training strategies and suitable ResNet hyperparameters. Models and code are available at https://github.com/Nikunj-Gupta/Efficient_ResNets.

翻译：ResNets（残差网络）是图像分类任务中最常用的模型之一。在本项目中，我们设计并训练了一个改进的ResNet模型用于CIFAR-10图像分类。具体而言，我们的目标是在保持ResNet模型规模不超过500万可训练参数的指定固定预算下，最大化CIFAR-10基准测试的准确率。模型规模（通常以可训练参数数量衡量）在需要将模型存储于存储容量有限的设备（如物联网/边缘设备）时至关重要。本文介绍了一种参数规模低于500万的残差网络设计。实验表明，结合多种训练策略和合适的ResNet超参数后，我们的ResNet在CIFAR-10上达到了96.04%的测试准确率，显著高于包含超过1100万可训练参数的ResNet18。模型与代码已开源至https://github.com/Nikunj-Gupta/Efficient_ResNets。