Sample and Computation Redistribution for Efficient Face Detection
Authors: Jia Guo, Jiankang Deng, Alexandros Lattas, Stefanos Zafeiriou
ICLR 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on WIDER FACE demonstrate the state-of-the-art accuracy-efficiency trade-off for the proposed SCRFD family across a wide range of compute regimes. |
| Researcher Affiliation | Collaboration | Jia Guo2, Jiankang Deng1,2 , Alexandros Lattas1,3, Stefanos Zafeiriou1,3 1Huawei, 2Insight Face, 3Imperial College London {guojia,jiankangdeng}@gmail.com, {a.lattas,s.zafeiriou}@imperial.ac.uk |
| Pseudocode | Yes | Algorithm 1: Search algorithm for computation redistribution |
| Open Source Code | Yes | Code is available at: https://github.com/deepinsight/insightface/ tree/master/detection/scrfd. |
| Open Datasets | Yes | train each model on the WIDER FACE (Yang et al., 2016) training set for 80 epochs. |
| Dataset Splits | Yes | The WIDER FACE dataset is split into training (40%), validation (10%) and testing (50%) subsets by randomly sampling from 61 scene categories. |
| Hardware Specification | Yes | Our experiments are implemented in Py Torch... and train on eight Tesla V100. |
| Software Dependencies | No | Our experiments are implemented in Py Torch, based on the open-source MMDetection (Chen et al., 2019a). However, specific version numbers for PyTorch or MMDetection are not provided. |
| Experiment Setup | Yes | We adopt the SGD optimizer (momentum 0.9, weight decay 5e-4) with a batch size of 8 × 8 and train on eight Tesla V100. The learning rate is linearly warmed up to 0.015 within the first 3 epochs. During network search, the learning rate is multiplied by 0.1 at the 55-th, and 68-th epochs. The learning process terminates on the 80-th epoch. For training of both baselines and searched configurations, the learning rate decays by a factor of 10 at the 440-th and 544-th epochs, and the learning process terminates at the 640-th epoch. |