MaskFactory: Towards High-quality Synthetic Data Generation for Dichotomous Image Segmentation
Authors: Haotian Qian, Yinda Chen, Shengtao Lou, Fahad Shahbaz Khan, Xiaogang Jin, Deng-Ping Fan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on the widely-used DIS5K dataset benchmark demonstrate superior performance in quality and efficiency compared to existing methods. |
| Researcher Affiliation | Academia | 1State Key Lab of CAD&CG, Zhejiang University 2VCIP&CS, Nankai University 3MBZUAI 4Linköping University |
| Pseudocode | Yes | Appendix A Pseudocode for the Mask Factory Algorithm. Algorithm 1: Mask Factory Algorithm |
| Open Source Code | Yes | The code is available at https: //qian-hao-tian.github.io/Mask Factory/. |
| Open Datasets | Yes | We conduct our experiments on the DIS5K dataset, which comprises 5,479 high-resolution images... The DIS5K dataset is divided into three subsets: DIS-TR (3,000 images) for training, DIS-VD (470 images) for validation, and DIS-TE (2,000 images) for testing. |
| Dataset Splits | Yes | The DIS5K dataset is divided into three subsets: DIS-TR (3,000 images) for training, DIS-VD (470 images) for validation, and DIS-TE (2,000 images) for testing. |
| Hardware Specification | Yes | Our image editing framework is implemented using Py Torch and trained on 8 NVIDIA Ge Force RTX 3090 GPUs. We train the segmentation model using the DIS-TR subset of the DIS5K dataset, utilizing 2 NVIDIA Ge Force RTX 3090 GPUs. |
| Software Dependencies | No | Only |
| Experiment Setup | Yes | The hyperparameters used in our model are as follows: a batch size of 16, an image size of 512x512, 5 editing iterations, a learning rate of 0.001, a weight decay of 0.0001, 1000 diffusion steps, and a diffusion step size of 0.1. The hyperparameters in Equ 3 are set to λ1 = 0.8 and λ2 = 0.5. ... The input size to the network is 512x512, with a learning rate of 0.0001 and a batch size of 48. The model is optimized using the Adam optimizer over a total of 800 epochs. |