Unbiased Teacher for Semi-Supervised Object Detection
Authors: Yen-Cheng Liu, Chih-Yao Ma, Zijian He, Chia-Wen Kuo, Kan Chen, Peizhao Zhang, Bichen Wu, Zsolt Kira, Peter Vajda
ICLR 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We benchmark Unbiased Teacher with SSL setting using the MS-COCO and PASCAL VOC datasets, namely COCO-standard, COCO-additional, and VOC. When using only 1% labeled data from MS-COCO (COCO-standard), Unbiased Teacher achieves 6.8 absolute m AP improvement against the state-of-the-art method, STAC (Sohn et al., 2020b). Unbiased Teacher consistently achieves around 10 m AP improvements when using only 0.5, 1, 2, 5% of labeled data on MS-COCO. We also provide an ablation study to verify the effectiveness of each proposed component. |
| Researcher Affiliation | Collaboration | Yen-Cheng Liu1,2 , Chih-Yao Ma2, Zijian He2, Chia-Wen Kuo1, Kan Chen2, Peizhao Zhang2, Bichen Wu2, Zsolt Kira1, Peter Vajda2 1Georgia Tech, 2Facebook Inc. {ycliu,cwkuo,zkira}@gatech.edu, {cyma,zijian,kanchen18,stzpz,wbc,vajdap}@fb.com |
| Pseudocode | No | The paper describes methods and processes but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | Yes | 1Code: https://github.com/facebookresearch/unbiased-teacher. |
| Open Datasets | Yes | Datasets. We benchmark our proposed method on experimental settings using MS-COCO (Lin et al., 2014) and PASCAL VOC (Everingham et al., 2010) following existing works (Jeong et al., 2019; Sohn et al., 2020b). |
| Dataset Splits | Yes | Specifically, there are three experimental settings: (1) COCO-standard: we randomly sample 0.5, 1, 2, 5, and 10% of labeled training data as a labeled set and use the rest of the data as the training unlabeled set. Figure 2: Validation Losses of our model and the model trained with labeled data only. Figure 9: (a) Validation AP and (b) number of pseudo-label bounding boxes per image with various pseudo-labeling thresholds δ. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper states 'Our implementation builds upon the Detectron2 framework (Wu et al., 2019)' but does not specify version numbers for Detectron2 or any other software dependencies. |
| Experiment Setup | Yes | Implementation Details. For a fair comparison, we follow STAC (Sohn et al., 2020b) to use Faster RCNN with FPN (Lin et al., 2017a) and Res Net-50 backbone (He et al., 2016) as our object detectior, where the feature weights are initialized by the Image Net-pretrained model, same as existing works (Jeong et al., 2019; Sohn et al., 2020b). We use confidence threshold δ = 0.7. ... Hyper-parameters. We use confidence threshold δ = 0.7 to generate pseudo-labels for all our experiments, the unsupervised loss weight λu = 4 is applied for COCO-standard and VOC, and the unsupervised loss weight λu = 2 is applied for COCO-additional. We apply α = 0.9996 as the EMA rate for all our experiments. Hyper-parameters used are summarized in Table 5. Training. ... We use the SGD optimizer with a momentum rate 0.9 and a learning rate 0.01, and we use constant learning rate scheduler. The batch size of supervised and unsupervised data are both 32 images. For the COCO-standard, we train 180k iterations... |