FasterSeg: Searching for Faster Real-time Semantic Segmentation
Authors: Wuyang Chen, Xinyu Gong, Xianming Liu, Qian Zhang, Yuan Li, Zhangyang Wang
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on popular segmentation benchmarks demonstrate the competency of Faster Seg. For example, Faster Seg can run over 30% faster than the closest manually designed competitor on Cityscapes, while maintaining comparable accuracy. |
| Researcher Affiliation | Collaboration | Wuyang Chen1 , Xinyu Gong1 , Xianming Liu2, Qian Zhang2, Yuan Li2, Zhangyang Wang1 1Department of Computer Science and Engineering, Texas A&M University 2Horizon Robotics Inc. {wuyang.chen,xy gong,atlaswang}@tamu.edu {xianming.liu,qian01.zhang,yuan.li}@horizon.ai |
| Pseudocode | No | No explicit pseudocode or algorithm block found. |
| Open Source Code | Yes | Our framework is implemented with Py Torch. The search, training, and latency measurement codes are available at https://github.com/TAMU-VITA/Faster Seg. |
| Open Datasets | Yes | We use the Cityscapes (Cordts et al., 2016) as a testbed for both our architecture search and ablation studies. Cam Vid (Brostow et al., 2008). BDD (Yu et al., 2018b). |
| Dataset Splits | Yes | Cityscapes (Cordts et al., 2016) ... 2,975 images for training and 500 images for validation. Cam Vid (Brostow et al., 2008) ... 367 for training, 101 for validation and 233 for testing. BDD (Yu et al., 2018b) ... 7,000 images for training and 1,000 for validation. |
| Hardware Specification | Yes | In all experiments, we use Nvidia Geforce GTX 1080Ti for benchmarking the computing power. |
| Software Dependencies | Yes | We employ the high-performance inference framework Tensor RT v5.1.5 and report the inference speed. All experiments are performed under CUDA 10.0 and CUDNN V7. Our framework is implemented with Py Torch. |
| Experiment Setup | Yes | We use 160 × 320 random image crops from half-resolution (512 × 1024) images in the training set. When learning network weights W, we use SGD optimizer with momentum 0.9 and weight decay of 5 × 10−4. We used the exponential learning rate decay of power 0.99. When learning the architecture parameters α, β, andγ, we use Adam optimizer with learning rate 3 × 10−4. The entire architecture search optimization takes about 2 days on one 1080Ti GPU. |