SOLQ: Segmenting Objects by Learning Queries
Authors: Bin Dong, Fangao Zeng, Tiancai Wang, Xiangyu Zhang, Yichen Wei
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments |
| Researcher Affiliation | Industry | Bin Dong Fangao Zeng Tiancai Wang Xiangyu Zhang Yichen Wei MEGVII Technology {dongbin,zengfangao,wangtiancai,zhangxiangyu,weiyichen}@megvii.com |
| Pseudocode | No | The paper describes methods and processes but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github. com/megvii-research/SOLQ. |
| Open Datasets | Yes | We validate our method on COCO benchmark [19]. COCO contains 115k images for training, 5k for validation and 20k for testing, involving 80 object categories with instance-level segmentation annotations. |
| Dataset Splits | Yes | COCO contains 115k images for training, 5k for validation and 20k for testing, involving 80 object categories with instance-level segmentation annotations. |
| Hardware Specification | Yes | All experiments are conducted over 8 Tesla V100 GPUs with batch size 32 except the comparison in Sec. 4.4. |
| Software Dependencies | No | The paper mentions software components like ResNet, Deformable DETR, and Adam optimizer but does not provide specific version numbers for these or other key software dependencies. |
| Experiment Setup | Yes | For the mask branch, nk is set to 256, hidden dim of MLP is 1024 and λvec is 3.0. Following DETR, λcls = 2, λL1 = 5, λgiou = 2. We train our model with Adam optimizer with weight decay of 1.0 10 4. Models are trained for 50 epochs with the initial learning rate 2.0 10 4 and decayed at 40th epoch by a factor 0.1. Multi-scale training is adopted, where the shorter side is randomly chosen within [408, 800] and the longer side is less or equal to 1333. All experiments are conducted over 8 Tesla V100 GPUs with batch size 32 except the comparison in Sec. 4.4. |