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..
A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection
Authors: Kemal Oksuz, Baris Can Cam, Emre Akbas, Sinan Kalkan
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experiments, Dataset: We train all our models on COCO trainval35K set [15] (115K images), test on minival set (5k images) and compare with the state-of-the-art (SOTA) on test-dev set (20K images)., Table 2: Ablation analysis on COCO minival., Table 6: Comparison with the SOTA detectors on COCO test-dev. |
| Researcher Affiliation | Academia | Kemal Oksuz, Baris Can Cam, Emre Akbas , Sinan Kalkan Dept. of Computer Engineering, Middle East Technical University Ankara, Turkey EMAIL |
| Pseudocode | Yes | Algorithm 1 Obtaining the gradients of a ranking-based function with error-driven update. |
| Open Source Code | Yes | Code available at: https://github.com/kemaloksuz/aLRPLoss. |
| Open Datasets | Yes | Dataset: We train all our models on COCO trainval35K set [15] (115K images)... and Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Dollár P, Zitnick CL (2014) Microsoft COCO: Common Objects in Context. In: The European Conference on Computer Vision (ECCV) |
| Dataset Splits | Yes | Dataset: We train all our models on COCO trainval35K set [15] (115K images), test on minival set (5k images) and compare with the state-of-the-art (SOTA) on test-dev set (20K images). |
| Hardware Specification | Yes | For training, we use 4 v100 GPUs. |
| Software Dependencies | No | The paper mentions using the 'mmdetection framework [6]' but does not specify its version number or other software dependencies with explicit version details (e.g., Python, PyTorch, CUDA versions). |
| Experiment Setup | Yes | Implementation Details: For training, we use 4 v100 GPUs. The batch size is 32 for training with 512 x 512 images (a LRPLoss500), whereas it is 16 for 800 x 800 images (a LRPLoss800). Following AP Loss, our models are trained for 100 epochs using stochastic gradient descent with a momentum factor of 0.9. We use a learning rate of 0.008 for a LRPLoss500 and 0.004 for a LRPLoss800, each decreased by factor 0.1 at epochs 60 and 80. |