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 | Conference PDF | Archive PDF | Plain Text | 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 {kemal.oksuz, can.cam, eakbas, skalkan}@metu.edu.tr |
| 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. |