Dual Path Networks
Authors: Yunpeng Chen, Jianan Li, Huaxin Xiao, Xiaojie Jin, Shuicheng Yan, Jiashi Feng
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three benchmark datasets, Imag Net-1k, Places365 and PASCAL VOC, clearly demonstrate superior performance of the proposed DPN over state-of-the-arts. |
| Researcher Affiliation | Collaboration | National University of Singapore Beijing Institute of Technology National University of Defense Technology Qihoo 360 AI Institute |
| Pseudocode | No | No pseudocode or algorithm blocks were found. |
| Open Source Code | No | The paper does not provide any statement or link regarding the open-sourcing of the code for the described methodology. |
| Open Datasets | Yes | Extensive experiments are conducted for evaluating the proposed Dual Path Networks. Specifically, we evaluate the proposed architecture on three tasks: image classification, object detection and semantic segmentation, using three standard benchmark datasets: the Image Net-1k dataset, Places365-Standard dataset and the PASCAL VOC datasets. |
| Dataset Splits | No | The paper mentions using 'validation set' for evaluation (e.g., 'Single crop validation error rate (%) on validation set' in Table 2), but does not explicitly provide the specific percentages or sample counts for the train/validation/test splits, nor does it cite a source defining those exact splits for reproduction within the main text. |
| Hardware Specification | Yes | We implement the DPNs using MXNet [2] on a cluster with 40 K80 graphic cards. |
| Software Dependencies | No | The paper mentions using 'MXNet [2]' for implementation but does not specify the version number of MXNet or any other software dependencies. |
| Experiment Setup | Yes | Following [3], we adopt standard data augmentation methods and train the networks using SGD with a mini-batch size of 32 for each GPU. For the deepest network, i.e. DPN-131, the mini-batch size is limited to 24 because of the 12GB GPU memory constraint. The learning rate starts from 0.1 for DPN-92 and DPN-131, and from 0.4 for DPN-98. It drops in a steps manner by a factor of 0.1. Following [5], batch normalization layers are refined after training. |