LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation
Abstract: Pixel-wise semantic segmentation for visual scene understanding not only needs to be accurate but also efficient in order to find any use in real-time application. Existing algorithms even though are accurate but they do not focus on utilizing the parameters of neural network efficiently. As a result, they are huge in terms of parameters and number of operations; hence slow too. In this paper, we propose a novel deep neural network architecture which allows it to learn without any significant increase in the number of parameters. Our network uses only 11.5 million parameters and 21.2 GFLOPs for processing an image of resolution 3x640x360. It gives state-of-the-art performance on CamVid and comparable results on Cityscapes dataset. We also compare our networks processing time on NVIDIA GPU and embedded system device with existing state-of-the-art architectures for different image resolutions.
https://arxiv.org/abs/1707.03718
Task:
- Implement the LinkNet network architecture
- Show relevant metrics such as iOU and loss curves
- Show qualitative results on the validation/test datasets.
Dataset:
Cityscapes: https://www.cityscapes-dataset.com
Framework: You cannot use FastAI’s unet_learner. Any other framework is allowed.