Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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. |