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..
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Authors: Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, Lei Zhang1792-1800
AAAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experimental results are very encouraging, demonstrating the effectiveness of our proposed IA-YOLO method in both foggy and low-light scenarios. |
| Researcher Affiliation | Collaboration | Wenyu Liu1,2*, Gaofeng Ren 3, Runsheng Yu 4, Shi Guo 5, Jianke Zhu 1,2 , Lei Zhang 3,5 1 Colleage of Computer Science and Technology, Zhejiang University 2 Alibaba-Zhejiang University Joint Institute of Frontier Technologies 3 DAMO Academy, Alibaba Group 4 The Hong Kong University of Science and Technology 5 The Hong Kong Polytechnic University |
| Pseudocode | Yes | Algorithm 1: Image-Adaptive YOLO training procedure |
| Open Source Code | Yes | The source code can be found at https://github.com/wenyyu/ImageAdaptive-YOLO. |
| Open Datasets | Yes | We build upon the classic VOC dataset (Everingham et al. 2010) a VOC_Foggy dataset according to the atmospheric scattering model (Narasimhan and Nayar 2002). Moreover, RTTS (Li et al. 2018) is a relatively comprehensive real-world dataset available in foggy conditions... PSCAL VOC (Everingham et al. 2010) and the relatively comprehensive low-light detection dataset Ex Dark (Loh and Chan 2019) both contain ten categories of objects... |
| Dataset Splits | No | The paper describes how training and test sets (e.g., VOC_norm_trainval, VOC_norm_test) are formed. However, it does not provide specific details on a separate validation set split (e.g., percentages or counts) needed to reproduce the data partitioning for validation. |
| Hardware Specification | Yes | We use Tensorflow for our experiments and run it on the Tesla V100 GPU. |
| Software Dependencies | No | The paper mentions 'Tensorflow' but does not specify a version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | The backbone network for all experiments is Darknet-53. During training, We randomly resize the image to (32N 32N), where N [9, 19]. Moreover, the data augmentation methods like image flipping, cropping and transformation are applied to expand the training dataset. Our IA-YOLO model is trained by the Adam optimizer (Kingma and Ba 2014) with 80 epochs. The starting learning rate is 10 4 and the batch size is 6. |