论文标题
使用量表：在密集包装的场景中进行产品检测的第二位解决方案[技术报告]
Working with scale: 2nd place solution to Product Detection in Densely Packed Scenes [Technical Report]
论文作者
论文摘要
该报告描述了在CVPR 2020年零售 - 视觉研讨会中举行的检测挑战的第二名解决方案。这项工作并没有进一步考虑以前的结果，而是旨在通过重新实验来验证先前观察到的外卖。通过合并流行的对象检测工具箱-MMDetection来达到结果的可靠性和可重复性。在本报告中，我首先代表了更快的RCNN和视网膜模型所收到的结果，这些结果是在原始工作中进行比较的。然后，我用更高级的模型描述了实验结果。最后一部分回顾了用于更快的RCNN模型的两个简单技巧，这些技巧用于我的最终提交：更改默认的锚刻度刻度参数和火车时间图像瓷砖。源代码可在https://github.com/tyomj/product_detection上找到。
This report describes a 2nd place solution of the detection challenge which is held within CVPR 2020 Retail-Vision workshop. Instead of going further considering previous results this work mainly aims to verify previously observed takeaways by re-experimenting. The reliability and reproducibility of the results are reached by incorporating a popular object detection toolbox - MMDetection. In this report, I firstly represent the results received for Faster-RCNN and RetinaNet models, which were taken for comparison in the original work. Then I describe the experiment results with more advanced models. The final section reviews two simple tricks for Faster-RCNN model that were used for my final submission: changing default anchor scale parameter and train-time image tiling. The source code is available at https://github.com/tyomj/product_detection.
