You Only Look Around: Learning Illumination-Invariant Feature for Low-light Object Detection
Authors: Mingbo Hong, Shen Cheng, Haibin Huang, Haoqiang Fan, Shuaicheng Liu
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our empirical findings reveal significant improvements in low-light object detection tasks, as well as promising results in both well-lit and over-lit scenarios. |
| Researcher Affiliation | Collaboration | Mingbo Hong Megvii Technology, Beijing, China mingbohong97@gmail.com Shen Cheng Megvii Technology, Beijing, China chengshen@megvii.com Haibin Huang Kuaishou Technology jackiehuanghaibin@gmail.com Haoqiang Fan Megvii Technology, Beijing, China fhq@megvii.com Shuaicheng Liu University of Electronic Science and Technology of China liushuaicheng@uestc.edu.cn |
| Pseudocode | No | The paper describes its methods verbally and mathematically but does not include any clearly labeled pseudocode blocks or algorithms in a structured format. |
| Open Source Code | Yes | Code is available at https://github.com/Mingbo Hong/YOLA. |
| Open Datasets | Yes | We evaluate our proposed method on both real-world scenarios datasets: exclusively dark [32] (Ex Dark) and UG2+DARK FACE [48]. |
| Dataset Splits | Yes | Ex Dark dataset contains 7363 images ranging from lowlight environments to twilight, including 12 categories, 3,000 images for training, 1,800 images for validation, and 2,563 images for testing. |
| Hardware Specification | Yes | Specifically, we trained 12 epochs with 8 GPUs and a mini-batch of 1 per GPU in an initial learning rate of 1e-2 by the SGD optimizer on both well-lit and over-lit (generated by brightening the origin image) scenarios. |
| Software Dependencies | No | We implement YOLA using the MMDetection toolbox [4]. The paper mentions a software toolbox but does not specify its version number, nor does it list versions for other common dependencies like Python or PyTorch. |
| Experiment Setup | Yes | Both detectors are initially pre-trained on the COCO dataset and subsequently fine-tuned on the target datasets utilizing the SGD [41] optimizer with an initial learning rate of 1e-3. Specifically, we resize the Ex Dark dataset images to 608 608 and train both detectors for 24 epochs, reducing the learning rate by a factor of 10 at epochs 18 and 23. |