https://github.com/michuanhaohao/reid-strong-baseline 是用于行人重识别的代码,它是论文Bag of Tricks and a Strong Baseline for Deep Person Re-Identification的对应代码,这篇论文改进了standard baseline方法,达到了更好的行人重识别效果。以下是梳理的训练部分代码的内容 二、准备数据集 数据集加载的具体实现...
代码链接:https://github.com/michuanhaohao/reid-strong-baseline 作者:Hao Luo1∗, Youzhi Gu1∗, Xingyu Liao2∗, Shenqi Lai3, Wei Jiang1 1 Zhejiang University, 2 Chinese Academy of Sciences, 3 Xi’an Jiaotong University Motivation [1] We surveyed many works published on top conferences ...
Bag of Tricks and A Strong Baseline for Deep Person Re-identification Hao Luo, Youzhi Gu, Xingyu Liao, Shenqi Lai, Wei Jiang Zhejiang University, Chinese Academy of Sciences, Xi’an Jiaotong University 论文地址:arxiv.org/pdf/1903.0707 摘要 本文提出一个ReID中简单且有效的baseline。本文使用一些tric...
这样跑几轮,大概 2、3 个小时(当时我让epoch=25epoch=25),就跑出来 private 0.77 / public 0.79(所以后来四天我就让 public 提升了 0.02?) 感觉到 strong baseline 就只需要把训练时间延长就行了,也试了不同的模型 & loss 函数,感觉差距也不是很大。单个模型延长到epoch=100epoch=100,1-fold 跑出来就 0....
这个作业因为是在台大自己的 OJ 上交,因此没法看到评分了,不过把 strong baseline 所要求的的 weight clipping 和 WGAN-GP 都实作了一下,效果确实比一开始要好。。 Simple: 没有人型,不放了 Medium: Strong: (比 medium 的人型还是要多的 233)
https://github.com/michuanhaohao/reid-strong-baseline 首先作者在Maket1501数据集上对比了ECCV2018和CVPR2018的一些baseline的性能,并与自己提出的baseline作对比,这些baseline大部分都是通过一些训练技巧来提升的,由于在论文中轻描淡写易被忽视,因此作者也建议评价一篇学术论文一定要把训练技巧考虑在内,这样才会更加...
Run git clone https://github.com/michuanhaohao/reid-strong-baseline.git Install dependencies: pytorch>=0.4 torchvision ignite yacs Prepare dataset Create a directory to store reid datasets under this repo or outside this repo. Remember to set your path to the root of the dataset in config/de...
Error Threshold of SYK Codes from Strong-to-Weak Parity Symmetry Breaking 12 p. What is the origin of the JWST SMBHs? 11 p. URAvatar: Universal Relightable Gaussian Codec Avatars 28 p. Robust Gaussian Processes via Relevance Pursuit 12 p. EgoMimic: Scaling Imitation Learning via Egocentri...
论文阅读6 | Bag of Tricks and A Strong Baseline for Deep Person Re-identification,程序员大本营,技术文章内容聚合第一站。
This strong baseline network is paramount to the advancement of the field and to ensure the fair evaluation of algorithmic effectiveness. Second, we propose the Cross-Modality Contrastive Learning (CMCL) scheme, a novel approach to address the cross-modality discrepancies and enhance the quality of...