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..
MetaAnchor: Learning to Detect Objects with Customized Anchors
Authors: Tong Yang, Xiangyu Zhang, Zeming Li, Wenqiang Zhang, Jian Sun
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiment on COCO detection task shows that Meta Anchor consistently outperforms the counterparts in various scenarios. |
| Researcher Affiliation | Collaboration | Tong Yang Xiangyu Zhang Zeming Li Wenqiang Zhang Jian Sun Megvii Inc (Face++) EMAIL Fudan University EMAIL |
| Pseudocode | No | The paper describes the methodology using text and mathematical equations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | For YOLOv2 baseline, we use anchors showed on open source project4 to detect objects. 4https://github.com/pjreddie/darknet |
| Open Datasets | Yes | In this section we mainly evaluate our proposed Meta Anchor on COCO object detection task [24]. The basic detection framework is Retina Net [23] as introduced in 3.2, whose backbone feature extractor we use is Res Net-50 [13] pretrained on Image Net classification dataset [34]. |
| Dataset Splits | Yes | Following the common practice [23] in COCO detection task, for training we use two different dataset splits: COCO-all and COCO-mini; while for test, all results are evaluated on the minival set which contains 5000 images. COCO-all includes all the images in the original training and validation sets excluding minival images, while COCO-mini is a subset of around 20000 images. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions frameworks like Retina Net and YOLOv2, and refers to a 'Darknet' open source project, but does not provide specific version numbers for software dependencies or libraries. |
| Experiment Setup | Yes | For fair comparison, we follow most of the settings in [23] (image size, learning rate, etc.) for all the experiments, except for a few differences as follows. In [23], 3 3 anchor boxes (i.e. 3 scales and 3 aspect ratios) are predefined for each level of detection head...the number of hidden neurons m is set to 128. |