颜色分类leetcode-xview2_1st_place_solution:“xView2:评估建筑损坏”挑战的第一名解决方案

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颜色分类leetcode xview2 第一名解决方案 “xView2:评估建筑损坏”挑战的第一名解决方案。 解决方案介绍 使用此环境开发的解决方案: Python 3(基于Anaconda安装) Pytorch 1.1.0+ 和 torchvision 0.3.0+ 英伟达顶点 硬件:当前的训练批量大小至少需要 2 个 GPU,每个 GPU 为 12GB。 (最初在 Titan V GPU 上训练)。 对于 1 GPU 批量大小和学习率应该在实践中找到并相应地改变。 竞赛数据集中的“train”、“tier3”和“test”文件夹应放在当前文件夹中。 使用“train.sh”脚本来训练所有模型。 (在 2 个 GPU 上约 7 天)。 要生成预测/提交文件,请使用“predict.sh”。 “evaluation-docker-container”文件夹包含用于对保留集(CPU 版本)进行最终评估的 docker 容器的代码。 训练模型 此处提供经过训练的模型权重: (请注意:代码是在比赛期间开发的,旨在对不同的模型进行单独的实验。因此,按原样发布,没有额外的重构以提供完全的训练重现

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