CircleNet for Hip Landmark Detection
Authors: Hai Wu, Hongtao Xie, Chuanbin Liu, Zheng-Jun Zha, Jun Sun, Yongdong Zhang12370-12377
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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. |