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..
Domain Generalised Faster R-CNN
Authors: Karthik Seemakurthy, Charles Fox, Erchan Aptoula, Petra Bosilj
AAAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The proposed approach is implemented as a Domain Generalised Faster R-CNN and evaluated using four object detection datasets which provide domain metadata (GWHD, Cityscapes, BDD100K, Sim10K) where it exhibits a consistent performance improvement over the baselines. All the codes for replicating the results in this paper can be found at https://github.com/karthikiitm87/domain-generalisation.git |
| Researcher Affiliation | Academia | 1Lincoln Institute of Agri-Food Technology, University of Lincoln, United Kingdom. 2School of Computer Science, University of Lincoln, United Kingdom. 3Faculty of Engineering and Natural Sciences (VPALab), Sabanci University, Türkiye. |
| Pseudocode | Yes | Algorithm 1: Training strategy for domain generalised object detection. |
| Open Source Code | Yes | All the codes for replicating the results in this paper can be found at https://github.com/karthikiitm87/domain-generalisation.git |
| Open Datasets | Yes | Datasets. We demonstrate the generalisation ability of our approach on the following four popular multi-source object detection datasets. GWHD (David et al. 2021): ... Cityscapes (C) (Cordts et al. 2016) ... BDD100k (B) (Yu et al. 2020): ... Sim10k (S) (Johnson-Roberson et al. 2017): |
| Dataset Splits | Yes | GWHD (David et al. 2021): ... The training set has 18 domains with a total of 2943 images while the validation set contains samples captured across 8 different sessions with 1424 images and the test set has data from 21 different sessions with a total of 1434 images. Cityscapes (Cordts et al. 2016): It has a total of 2975 training (from 18 cities) and 500 validation images (from 3 cities). BDD100k (B) (Yu et al. 2020): The train, validation, and test splits has 70K, 10K, 20K images, respectively. Sim10k (S) (Johnson-Roberson et al. 2017): There is no official train and validation splits available for this dataset and hence we randomly split it into 8K images as training set and the rest as validation split. |
| Hardware Specification | Yes | The experiments were implemented using the Py Torch deep learning framework and Torchvision Faster R-CNN library on a NVIDIA RTX 3090 GPU with 24GB of GPU memory. |
| Software Dependencies | No | The paper mentions 'Py Torch deep learning framework and Torchvision Faster R-CNN library' but does not specify version numbers for these software components. |
| Experiment Setup | Yes | From empirical observations, the regularisation constants were set to α1 = 1, α2 = 0.1, α3 = 1, α4 = 0.001, and α5 = 0.05. We used early stopping with a patience of 10 epochs. Adam W (weight decay = 0.0005, learning rate = 0.001, batchsize=2) has been used as optimiser while training with GWHD and Stochastic Gradient Descent (SGD) (weight decay = 0.0005, momentum=0.9, learning rate=2 10 3, batchsize=2) has been used for other datasets (Cityscapes, BDD100K, Sim10K). |