SpeedDETR: Speed-aware Transformers for End-to-end Object Detection
Authors: Peiyan Dong, Zhenglun Kong, Xin Meng, Peng Zhang, Hao Tang, Yanzhi Wang, Chih-Hsien Chou
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the MS COCO dataset show Speed DETR outperforms current DETR-based methods by 1.5% 9.2% AP with 1.09 3.6 speedup on Tesla V100. |
| Researcher Affiliation | Collaboration | 1Northeastern University, Boston, MA, U.S.A 2Futurewei Technologies, Santa Clara, CA, U.S.A 3Peking University, Beijing, China 4Tsinghua University, Beijing, China 5CVL, ETH, Switzerland. |
| Pseudocode | Yes | Algorithm 1 Speed-aware Model Slimming. |
| Open Source Code | Yes | Codes release Speed DETR. |
| Open Datasets | Yes | Experiments on the MS COCO dataset show Speed DETR outperforms current DETR-based methods by 1.5% 9.2% AP with 1.09 3.6 speedup on Tesla V100. and The HOD backbone is pre-trained on Image Net (Deng et al., 2009) with the same setting as (Cao et al., 2021). |
| Dataset Splits | No | The paper mentions training epochs and optimizer settings but does not explicitly provide details about validation dataset splits or how validation was performed. |
| Hardware Specification | Yes | Experiments on the MS COCO dataset show Speed DETR outperforms current DETR-based methods by 1.5% 9.2% AP with 1.09 3.6 speedup on Tesla V100. Even acceptable speed inference can be achieved on edge GPUs, i.e., 4 FPS for NVIDIA JETSON TX2 (1.4 4 faster than other counterparts), 1 FPS for NVIDIA NANO (1.5 6.7 faster). and We test four types of hardware devices, and their properties are listed in Table 1. |
| Software Dependencies | No | The paper mentions 'Py Torch Profiler' but does not provide specific version numbers for software dependencies like PyTorch itself or other libraries used in implementation. |
| Experiment Setup | Yes | We use the Adam W optimizer with a batch size of 32, an initial learning rate of 1e 4, and a weight decay of 0.05. The learning rate is stepped down by a factor of 0.1 at the 67% and 89% of training epochs. |