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..
CircleNet for Hip Landmark Detection
Authors: Hai Wu, Hongtao Xie, Chuanbin Liu, Zheng-Jun Zha, Jun Sun, Yongdong Zhang12370-12377
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We construct a professional DDH dataset for the first time and evaluate our Circle Net on it. ... Our results show that the Circle Net can achieve the state-of-the-art results for landmark detection on the dataset with a large margin of 1.8 average pixels compared to current methods. |
| Researcher Affiliation | Collaboration | 1School of Information Science and Technology, University of Science and Technology of China 2Anhui Province Children s Hospital of China |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The dataset and source code will be publicly available. |
| Open Datasets | No | We construct a professional DDH dataset for the first time... Now, the dataset is available from authors upon reasonable request. |
| Dataset Splits | No | The paper states '7706 images are used for training and the rest 1826 images are for testing' but does not explicitly mention a validation split. |
| Hardware Specification | Yes | The Circle Net is trained using the Pytorch framework on a Ubuntu workstation equipped with an Intel i7-9700 CPU and two 11GB Nvidia Ge Force 1080Ti GPUs. |
| Software Dependencies | No | The paper mentions 'Pytorch framework' and 'Open CV' but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | During training, the mini batch size is set to 12. Adagrad optimizer is used for updating with the learning rate of 1.25e-4. The default training epoch is 30. During training, we resize the input resolution to 512 512. In our experiments, we adopt λr = 0.1 and λo = 1 as default setting. |