2020软件工程实验A组

小组成员

SY1906423 张崇智BY1906033 秦浩桐SY1906120 高明骏SY1906504 王茵迪SY1906426 赵永驰BY1906010 黄 涵SY1906420 吴振赫

项目定位

本次待开发的软件为基于PyTorch的前沿深度学习算法集成应用程序接口,该应用程序接口可在任何支持规定版本Python环境的计算终端进行安装和调用。目标用户为深度学习领域的科研人员,用户通过使用该应用程序接口在个人计算终端完成前沿深度学习算法的调用和模型及其他结果的生成。通过使用该软件,相关科研人员可以减少复现过往论文的工作,提高科研的效率和质量。

系统架构图

architecture

程序运行环境

  1. 操作系统:Linux为Ubuntu 16及以上版本、Windows7及以上版本、MacOS 10及以上版本
  2. CPU:英特尔i7-6700H等性能相当或更高配置的CPU
  3. GPU:英伟达GTX-1080TI等性能相当或更高配置的GPU
  4. 内存:16G或更高
  5. 硬盘存储:500G或更高

产品演示视频

Demo Video

参考资料

[1] GB-T8567-2006, 计算机软件文档编制规范[S].

[2] Roger S.Pressman著, 郑人杰等译.软件工程[M].第七版.北京:机械工业出版社,2011.

[3] PyTorch官方文档[OL]https://PyTorch.org/

[4] Siddhant A, Lipton Z C. Deep bayesian active learning for natural language processing: Results of a large-scale empirical study[J]. arXiv preprint arXiv:1808.05697, 2018.

[5] Sener O, Savarese S. Active learning for convolutional neural networks: A core-set approach[J]. arXiv preprint arXiv:1708.00489, 2017.

[6] Wang, Yizhong, et al. Multi-passage machine reading comprehension with cross-passage answer verification. [R]: Baidu Research, 2018

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[9] Ren S, He K, Girshick R, et al. Faster r-cnn: Towards real-time object detection with region proposal networks[C]//Advances in neural information processing systems. 2015: 91-99.

[10] Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2980-2988.

[11] Rastegari M, Ordonez V, Redmon J, et al. Xnor-net: Imagenet classification using binary convolutional neural networks[C]//European conference on computer vision. Springer, Cham, 2016: 525-542.

[12] Liu Z, Wu B, Luo W, et al. Bi-real net: Enhancing the performance of 1-bit cnns with improved representational capability and advanced training algorithm[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 722-737.