Presentation of our paper, "B-FPGM: Lightweight Face Detection via Bayesian-Optimized Soft FPGM Pruning", by N. Kaparinos and V. Mezaris. Presented at the RWS Workshop of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV 2025), Tucson, AZ, USA, Feb. 2025. Preprint and software available at http://arxiv.org/abs/2501.16917 https://github.com/IDT-ITI/B-FPGM

1. B-FPGM: Lightweight Face Detection via
Bayesian-Optimized Soft FPGM Pruning
Nikolaos Kaparinos, Vasileios Mezaris
CERTH-ITI, Thermi, Thessaloniki, Greece
Real-World Surveillance
Workshop @ WACV 2025

2. The Growing Demand for Compact AI Models
● The deployment of AI models on mobile devices, such as smartphones and
drones, is increasingly common.
● Thus, the need for compact and efficient AI models has dramatically
increased.
● Face detectors are a type of model commonly deployed on mobile devices.
● Lightweight face detectors have been proposed in the literature.
● They utilize lightweight backbone networks and other optimization techniques,
such as pruning.
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3. Network Pruning
● Network pruning is a technique used to reduce the number of parameters in a
model.
● Pruning methods can also be classified into uniform and non-uniform
approaches.
● FPGM pruning is a structured pruning approach that has demonstrated high
performance in the literature.
● Soft Filter Pruning (SFP) is a pruning method that allows the pruned filters to
be updated during subsequent training steps.
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4. B-FPGM
● This work proposes, B-FPGM, a novel non-uniform face detection network
pruning technique.
● This work represents the first application of Bayesian optimization to
structured pruning as well as non-uniform pruning in the literature.
● B-FPGM divides the network layers into 6 groups and employes Bayesian
optimization to optimize the pruning rate of each group.
● The optimal pruning rates are then applied alongside FPGM pruning and
SFP.
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5. B-FPGM Advantages
● B-FPGM offers flexibility through its non-universal pruning approach.
● It eliminates the need for engineering expertise to define rules for optimal
pruning rates, effectively taking the ‘human out of the loop’.
● At the same time, it avoids utilizing Reinforcement Learning, which comes
with significant drawbacks.
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6. B-FPGM overall pipeline
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7. Bayesian optimization step
● The Bayesian optimization step is employed to identify the optimal pruning
rate for each layer group, given a target overall sparsity.
● In each iteration, the pre-trained network is soft-pruned and trained for one
epoch.
● The objective function value is equal to the validation loss, plus an additional
term to ensure that the network is pruned approximately at the target overall
sparsity.
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8. Network Layer Groups
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The number of parameters
in each network layer group.
EResFD model architecture
and layer groups.

9. Overall B-FPGM algorithm
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10. Experimental Setup
● All our experiments were applied to EResFD, the currently smallest (in number of
parameters) well-performing face detector of the literature.
● A small ablation study with a second small face detector, EXTD, is also reported.
● The experiments were performed using the WIDER FACE dataset.
○ 12941 training images
○ Three validation subsets based on difficulty: Easy (1146 images), Medium (1079 images), Hard (1001
images)
● Experiments were conducted with target pruning rates ranging from 10% to 60%.
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11. Results on EResFD using the WIDER FACE dataset
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Hard Subset
Group pruning rates determined by Bayesian
optimization. T is the target pruning rate.
10%
10%
20%
20%
30%
30%
40%
40%
50%
50%
60%
60%

12. Comparison with SoA models
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13. Robustness to Randomness
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Mean mAP ± standard deviation of B-FPGM on
EResFD across five runs, using different random
seeds, for 20% target pruning rate.

14. Number of layer groups ablation
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MAP of B-FPGM on EResFD, on WIDER FACE (Easy, Medium,
Hard subsets), for different network layer groupings. N is the
number of layer groups and T is the target pruning rate.

15. Inference visual example
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EResFD 50% pruned using B-FPGM

16. Thank you for your attention!
Questions?
Nikolaos Kaparinos, kaparinos@iti.gr
Vasileios Mezaris, bmezaris@iti.gr
Source code and pruned models available at:
https://github.com/IDT-ITI/B-FPGM
This work was supported by the EU Horizon Europe and Horizon 2020 programmes
under grant agreements 101070093 vera.ai and 951911 AI4Media, respectively.
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