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..
Deformable Part Region Learning for Object Detection
Authors: Seung-Hwan Bae95-103
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Without bells and whistles, our implementation of a Cascade deformable part region detector achieves better detection and segmentation m APs on COCO and VOC datasets, compared to the recent cascade and other state-of-the-art detectors. |
| Researcher Affiliation | Academia | Seung-Hwan Bae Vision and Learning Laboratory, Inha University, Korea EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing open-source code for the described methodology or a direct link to a code repository. |
| Open Datasets | Yes | Our D-PRD and Cascade D-PRD are evaluated on MSCOCO17 (Lin et al. 2014) and PASCAL VOC07/12 (Everingham et al. 2015) datasets. |
| Dataset Splits | No | The paper mentions using COCO and VOC datasets and their trainval/test sets but does not explicitly provide specific percentages, sample counts for training, validation, and test splits, or explicit citations for the exact splits used for reproduction. |
| Hardware Specification | No | The paper does not specify any particular hardware used for running the experiments (e.g., specific GPU/CPU models, memory, or cloud instances). |
| Software Dependencies | No | The paper mentions "We use the Detectron2." but does not provide specific version numbers for Detectron2 or its underlying software dependencies like Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We use the default learning schedules 1x or 3x ( 12 or 37 COCO epochs) of Detectron2 for all the evaluation below. Also, all other setting parameters for training and testing are same to those of Detectron2. We set the Io U threshold to (0.5, 0.6, 0.7) from the ο¬rst to last stage. |