Progressive Transfer Learning for Person Re-identification
Authors: Zhengxu Yu, Zhongming Jin, Long Wei, Jishun Guo, Jianqiang Huang, Deng Cai, Xiaofei He, Xian-Sheng Hua
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments show that our proposal can improve the performance of the Re ID model greatly on MSMT17, Market1501, CUHK03 and Duke MTMC-re ID datasets. |
| Researcher Affiliation | Collaboration | 1 State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China, 2 DAMO Academy, Alibaba Group, Hangzhou, China, 3 Fabu Inc., Hangzhou, China, 4 GAC R&D Center, Guangzhou, China |
| Pseudocode | No | The paper includes mathematical equations (Eq. 1, Eq. 2) but does not present them or any other procedural steps in a formal pseudocode or algorithm block. |
| Open Source Code | No | The code will be released later on at https://github. com/ZJULearning/PTL |
| Open Datasets | Yes | We selected four persuasive Re ID datasets to evaluate our proposal, including Market-1501, Duke MTMC-re ID, MSMT17 and CUHK03. We followed the same dataset split by Wei et al. [Wei et al., 2018], and we also used the evaluation code provided by them (https://github.com/Join Wei-PKU/ MSMT17 Evaluation). For all experiments on Market-1501, Duke MTMCre ID and CUHK03, we used the evaluation code provided in Open-Re ID (https://github.com/Cysu/open-reid). |
| Dataset Splits | Yes | We followed the same dataset split by Wei et al. [Wei et al., 2018] [MSMT17]. We followed the same dataset split as used in the [Wang et al., 2018] [CUHK03]. All validation, query and gallery set of these two datasets are abandoned [Market-Duke]. |
| Hardware Specification | No | The paper mentions "GPU usage limitation" but does not specify any particular GPU models, CPU types, or other hardware components used for running the experiments. |
| Software Dependencies | No | The paper mentions using SGD-M as the optimizer and models like Dense Net-161 and Res Net-50, but it does not specify any software libraries, frameworks (e.g., PyTorch, TensorFlow), or their version numbers. |
| Experiment Setup | Yes | The initial learning rate is set to 0.01 and decay the learning rate ten times every ten epochs. Models are fine-tuned for 50 epochs. Unless otherwise stated, in all of our experiments, we use SGD-M as the optimizer. The hyper-parameter λ is set to 0.8 by practicing in the following experiments. Dense Net-161* used a batch size of 90, other experiments involving Dense Net-161 used a batch size of 32. |