DeepFacade: A Deep Learning Approach to Facade Parsing
Authors: Hantang Liu, Jialiang Zhang, Jianke Zhu, Steven C. H. Hoi
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method by training a FCN-8s network with the novel loss function. Experimental results show that our method has outperformed previous state-of-the-art methods significantly on both the ECP dataset and the e TRIMS dataset. |
| Researcher Affiliation | Collaboration | 1 College of Computer Science, Zhejiang University, China 2Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies 3School of Information Systems, Singapore Management University, Singapore |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We evaluate our porposed approach on two different datasets, the Ecole Centrale Paris (ECP) Facades dataset [Teboul et al., 2010] and the e TRIMS [Korˇc and Förstner, 2009] database. |
| Dataset Splits | Yes | 5-fold cross-validation is taken to generate proposals from all images. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions software components like 'VGG16', 'Faster R-CNN', 'RPN', and 'Adam optimizer' but does not provide specific version numbers for any of them. |
| Experiment Setup | Yes | The initial learning rate is set to 10 6. For the ECP dataset, training epoch is 100 and for the e TRIMS dataset training epoch is 80. ... Dropout [Srivastava et al., 2014] is used during training to prevent overfitting. ... For η in Equation 8, we set it to be 0.17 empirically. |