Dual-stream Network for Visual Recognition
Authors: Mingyuan Mao, peng gao, Renrui Zhang, Honghui Zheng, Teli Ma, Yan Peng, Errui Ding, Baochang Zhang, Shumin Han
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Without bells and whistles, the proposed DS-Net outperforms Dei T-Small by 2.4% in terms of top-1 accuracy on Image Net-1k and achieves state-of-the-art performance over other Vision Transformers and Res Nets. For object detection and instance segmentation, DS-Net-Small respectively outperforms Res Net-50 by 6.4% and 5.5 % in terms of m AP on MSCOCO 2017... 4 Experiments In this section, we first provide three ablation studies to explore the optimal structure of DS-Net and interpret the necessity of dual-stream design. Then we give the experimental results of image classification and downstream tasks including object detection and instance segmentation. |
| Researcher Affiliation | Collaboration | Mingyuan Mao1, , Peng Gao3, , Renrui Zhang2, , Honghui Zheng3, Teli Ma2, Yan Peng3, Errui Ding3, Baochang Zhang1,*, Shumin Han3,* 1Beihang University, Beijing, China 2Shanghai AI Laboratory, China 3Department of Computer Vision Technology (VIS), Baidu Inc |
| Pseudocode | No | No explicit pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The code will be released soon. |
| Open Datasets | Yes | We use Image Net-1K [8] for classification and MSCOCO 2017 [25] for object detection and instance segmentation. |
| Dataset Splits | Yes | Image Net-1K [8], comprising 1.28M training images and 50K validation images of 1000 classes. ... MSCOCO 2017 [25], containing 118K training images and 5K validation images. |
| Hardware Specification | Yes | All experiments are conducted on 8 V100 GPUs and the throughput is tested on 1 V100 GPU. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | We train our model for 300 epochs by Adam W optimizer. The initial learning rate is set to 1e-3 and scheduled by the cosine strategy. ... As the standard 1 schedule(12 epochs), we adopt Adam W optimizer with initial learning rate of 1e-4, decayed by 0.1 at epoch 8 and 11. We set stochastic drop path regularization of 0.1 and weight decay of 0.05. |