Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Sample and Computation Redistribution for Efficient Face Detection
Authors: Jia Guo, Jiankang Deng, Alexandros Lattas, Stefanos Zafeiriou
ICLR 2022 | Venue PDF | 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 EMAIL, EMAIL |
| 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. |