Consistency-based Semi-supervised Learning for Object detection
Authors: Jisoo Jeong, Seungeui Lee, Jeesoo Kim, Nojun Kwak
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have evaluated the proposed CSD both in single-stage and two-stage detectors and the results show the effectiveness of our method. |
| Researcher Affiliation | Academia | Jisoo Jeong , Seungeui Lee , Jeesoo Kim & Nojun Kwak Department of Transdisciplinary Studies Graduate School of Convergence Science and Technology Seoul National University Seoul, Korea |
| Pseudocode | No | The paper describes the method using textual descriptions, equations, and diagrams, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | 2https://github.com/soo89/CSD-SSD 3https://github.com/soo89/CSD-RFCN |
| Open Datasets | Yes | In our experiments, we have utilized the PASCAL VOC [22] and MSCOCO [23] datasets which are the most popular datasets in object detection. |
| Dataset Splits | Yes | PASCAL VOC 2007 and 2012 datasets consist of 5,011 and 11,540 trainval (train and validation) images respectively. In this paper, PASCAL VOC2007 trainval is used as the labeled data and PASCAL VOC2012 trainval and MSCOCO are utilized as the unlabeled one. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for experiments, such as GPU models or CPU types. |
| Software Dependencies | No | The paper mentions that the codes are based on PyTorch, and uses third-party codes for SSD and R-FCN, but it does not specify version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | No | The paper states that 'all the parameter settings and training details are presented in the supplementary material' and 'Details on learning scheduling are in the supplementary material,' but these specific details are not provided in the main text. |