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..
ViDT: An Efficient and Effective Fully Transformer-based Object Detector
Authors: Hwanjun Song, Deqing Sun, Sanghyuk Chun, Varun Jampani, Dongyoon Han, Byeongho Heo, Wonjae Kim, Ming-Hsuan Yang
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive evaluation results on the Microsoft COCO benchmark dataset demonstrate that Vi DT obtains the best AP and latency trade-off among existing fully transformer-based object detectors, and achieves 49.2AP owing to its high scalability for large models. |
| Researcher Affiliation | Collaboration | 1NAVER AI Lab 2Google Research 3University of California at Merced 4Yonsei University |
| Pseudocode | No | The paper includes figures illustrating architectures but no formal pseudocode or algorithm blocks with numbered steps formatted as code. |
| Open Source Code | Yes | We release the code and trained models at https://github.com/naver-ai/vidt. |
| Open Datasets | Yes | We carry out object detection experiments on the Microsoft COCO 2017 benchmark dataset (Lin et al., 2014). |
| Dataset Splits | Yes | All the fully transformer-based object detectors are trained on 118K training images and tested on 5K validation images following the literature (Carion et al., 2020). |
| Hardware Specification | Yes | All the algorithms are implemented using Py Torch and executed using eight NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions PyTorch and AdamW but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We train Vi DT for 50 epochs using Adam W (Loshchilov & Hutter, 2019) with the same initial learning rate of 10 4 for its body, neck and head. The learning rate is decayed by cosine annealing with batch size of 16, weight decay of 1 10 4, and gradient clipping of 0.1. |