Learning Fully Dense Neural Networks for Image Semantic Segmentation
Authors: Mingmin Zhen, Jinglu Wang, Lei Zhou, Tian Fang, Long Quan9283-9290
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have demonstrated the best performance of the FDNet on the two benchmark datasets: PASCAL VOC 2012, NYUDv2 over previous works when not considering training on other datasets. |
| Researcher Affiliation | Collaboration | Mingmin Zhen,1 Jinglu Wang,2 Lei Zhou,1 Tian Fang,3 Long Quan1 1Hong Kong University of Science and Technology, 2Microsoft Research Asia, 3Altizure.com {mzhen, lzhouai, quan}@cse.ust.hk, Jinglu.Wang@microsoft.com, fangtian@altizure.com |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | We conduct comprehensive experiments on PASCAL VOC 2012 dataset (Everingham et al. 2010) and NYUDv2 dataset (Silberman et al. 2012). |
| Dataset Splits | Yes | PASCAL VOC 2012: The dataset has 1,464 images for training, 1,449 images for validation and 1,456 images for testing, which involves 20 foreground object classes and one background class... NYUDv2: We use the standard training/test split with 795 and 654 images, respectively. |
| Hardware Specification | Yes | The proposed FDNet is implemented with PyTorch on a single NVIDIA GTX 1080Ti. |
| Software Dependencies | No | The paper mentions 'The proposed FDNet is implemented with PyTorch', but does not specify a version number for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | We train the dataset with 30K iterations. We optimize the network by using the poly learning rate policy where the initial learning rate is multiplied by (1 iter max iter)power with power = 0.9. The initial learning rate is set to 0.00025. We set momentum to 0.9 and weight decay to 0.0005. |