End-to-End Multi-Object Detection with a Regularized Mixture Model
Authors: Jaeyoung Yoo, Hojun Lee, Seunghyeon Seo, Inseop Chung, Nojun Kwak
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate D-RMM on MS COCO 2017 (Lin et al., 2014). Table 1 presents a comparison between Sparse R-CNN and D-RMM with different backbone networks on COCO validation set. |
| Researcher Affiliation | Collaboration | 1NAVER WEBTOON AI 2Department of Intelligence and Information Science, Seoul National University 3Interdisciplinary Program in Artificial Intelligence, Seoul National University. |
| Pseudocode | No | The paper describes the D-RMM framework and architecture using text and figures, but no explicit pseudocode or algorithm blocks are presented. |
| Open Source Code | Yes | Code is available at https://github.com/lhj815/D-RMM. |
| Open Datasets | Yes | Dataset. We evaluate D-RMM on MS COCO 2017 (Lin et al., 2014). |
| Dataset Splits | Yes | Following the common practice, we split the dataset into 118K images for the training set, 5K for the validation set, and 20K for the test-dev set. |
| Hardware Specification | Yes | FPS is measured as a network inference time excluding data loading on a single NVIDIA TITAN RTX using MMDet (Chen et al., 2019) with batch size 1. |
| Software Dependencies | No | The paper mentions using MMDet and Adam W optimizer, but does not provide specific version numbers for these or other software dependencies. |
| Experiment Setup | Yes | In training based on Sparse R-CNN, the batch size is 16. The identical data augmentations used in Deformable DETR (Zhu et al., 2020) are used for multi-scale training, where the input image size is 480 800 with random crop and random horizontal flip. We use Adam W (Loshchilov & Hutter, 2017) optimizer with a weight decay of 5e-5 and a gradient clipping with an L2 norm of 1.0. We adopt the training schedule of 36 epochs with an initial learning rate of 5e-5, divided by a factor of 10 at the 27th and 33rd epoch, respectively. |