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facedet

Name: facedet
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FaceDet — Production Face Detection for Video

SCRFD-family detectors + ByteTrack tracking + temporal smoothing Optimized for tiny faces, blur, occlusion, poor lighting, compression, and temporal stability.

Architecture Survey & Design Decisions

Ranked Candidate Models (WiderFace Hard AP)

Rank	Model	Easy	Medium	Hard	GFLOPs	FPS (V100)	Year	Selected?
1	ASFD-D6	97.2	96.5	92.5	~500	~7	2022	✗ (too slow)
2	TinaFace-R50+TTA	96.1	95.5	92.4	~42K (MS)	~3	2020	✗ (TTA-dependent)
3	TinaFace-R50 (single)	95.9	95.2	92.1	508	~15	2020	✗ (not efficient)
4	RetinaFace-R152+MS	96.9	96.1	91.8	High	13	2019	✗ (heavy backbone)
5	MOS-L (R152)	96.9	96.1	92.1	Multi-scale	~16	2021	✗ (heavy)
6	DSFD	96.6	95.7	90.4	~1532	—	2018	✗ (outdated)
7	SCRFD-34GF	96.1	95.0	85.2	34	~80	2021	✓ Flagship
8	SCRFD-10GF	95.2	93.9	83.1	10	~140	2021	✓ Balanced
9	SCRFD-2.5GF	93.8	92.2	77.9	2.5	~400	2021	✓ Real-time
10	SCRFD-0.5GF	90.6	88.1	68.5	0.5	~1000	2021	✓ Mobile
11	RetinaFace-MN0.25	91.4	90.1	78.2	~1	60 CPU	2019	✗ (SCRFD-2.5G better)
12	YuNet	85.6	84.2	72.7	2.5	77 CPU	2021	✗ (lower AP)

Why SCRFD?

The SCRFD family achieves the best accuracy-efficiency Pareto frontier for face detection. The key findings:

3.86% better Hard AP than TinaFace at 3× speed (SCRFD-34G vs TinaFace-R50)
No ImageNet pretraining needed — trains from scratch in 640 epochs
Scalable family — same architecture principles from 0.5 to 34 GFLOPs
Two orthogonal innovations: Sample Redistribution (augmentation) + Computation Redistribution (NAS)

Higher-ranked models (ASFD-D6, TinaFace+TTA) achieve marginally better Hard AP but at 10-100× the compute cost, making them impractical for video.

Key Technical Insights From Literature

Finding	Source	Impact
Large-scale crops [0.3–2.0] increase stride-8 positives from 72K→118K	SCRFD §3.2	+5-8% Hard AP
GFL jointly trains quality + classification → better score calibration	SCRFD §3.1	+1-2% Hard AP
Lower IoU matching threshold (0.35 vs 0.5) helps tiny faces	TinaFace §3.3	+2% Hard AP
GroupNorm > BatchNorm at small batch sizes	TinaFace §3.2	Stable training
5-point landmark auxiliary loss improves detection by ~1%	RetinaFace §4.2	+1% Hard AP
WiderFace Hard is near-saturated (~92% AP ceiling)	Survey '21	Focus on efficiency
No published improvements to WiderFace Hard since 2022	Literature scan	Benchmark mature

Model Zoo

Model	WiderFace (E/M/H)	GFLOPs	Params	FPS (V100 VGA)	Use Case
`scrfd_34g`	96.1/95.0/85.2	34	9.80M	~80	Flagship quality
`scrfd_10g`	95.2/93.9/83.1	10	3.86M	~140	Balanced
`scrfd_2.5g`	93.8/92.2/77.9	2.5	0.67M	~400	Real-time video
`scrfd_0.5g`	90.6/88.1/68.5	0.5	0.57M	~1000	Mobile/edge

Architecture

Input Image (640×640)
    │
    ▼
┌─────────────────────────────────────────┐
│  BACKBONE (NAS-searched ResNet-style)    │
│  ┌─────┐  ┌─────┐  ┌──────┐  ┌─────┐  │
│  │Stem │→ │ S1  │→ │  S2  │→ │ S3  │→ │ S4  │
│  │s=4  │  │s=4  │  │ s=8  │  │s=16 │  │s=32 │
│  └─────┘  └─────┘  └──┬───┘  └──┬──┘  └──┬──┘
│                        │ C3      │ C4     │ C5
└────────────────────────┼─────────┼────────┼──┘
                         │         │        │
    ┌────────────────────▼─────────▼────────▼──┐
    │  PAFPN (Path Aggregation FPN)             │
    │  Top-down (FPN) + Bottom-up (PAN)         │
    │  ┌────┐    ┌────┐    ┌────┐               │
    │  │ P3 │ ← │ P4 │ ← │ P5 │  (top-down)   │
    │  │ P3 │ → │ P4 │ → │ P5 │  (bottom-up)   │
    │  │s=8 │    │s=16│    │s=32│               │
    │  └──┬─┘    └──┬─┘    └──┬─┘               │
    └─────┼─────────┼─────────┼─────────────────┘
          │         │         │
    ┌─────▼─────────▼─────────▼─────────────────┐
    │  SHARED HEAD (per level, weight-shared)    │
    │  ┌──────────┐  ┌──────────┐               │
    │  │ CLS (GFL)│  │ REG(DIoU)│ [LMK (opt)]  │
    │  │ A×1      │  │ A×4      │ [A×10]        │
    │  └──────────┘  └──────────┘               │
    └───────────────────────────────────────────┘
          │                   │
          ▼                   ▼
    ┌─────────────┐    ┌──────────────┐
    │ ATSS Match  │    │ NMS (θ=0.4)  │
    │ (training)  │    │ (inference)  │
    └─────────────┘    └──────────────┘

Anchors (per level):

Stride 8: [16, 32] — small faces (≥16px)
Stride 16: [64, 128] — medium faces
Stride 32: [256, 512] — large faces
Aspect ratio: 1.0 (square — faces are roughly square)

Video Pipeline

Frame → Detector (SCRFD) → ByteTrack Tracker → Temporal Smoother → Output
         ↓                    ↓                    ↓
   Per-frame boxes      Track IDs (stable)   Jitter-free boxes
   + scores             + Kalman prediction   + Score momentum
   + landmarks          + 2-stage matching    + Adaptive EMA

ByteTrack (Zhang et al., 2022): Uses ALL detections — high + low confidence — for two-stage association. Low-confidence detections handle partially occluded faces that would be lost by traditional trackers.

Temporal Smoother: Adaptive EMA where smoothing factor scales with motion magnitude:

Static faces → heavy smoothing (α≈0.3) → no jitter
Fast-moving faces → light smoothing (α≈0.9) → no lag

Quick Start

Installation

pip install -r requirements.txt

Detect faces in a video

python

from facedet import VideoFaceDetector

detector = VideoFaceDetector(
    model_path='checkpoints/scrfd_34g_best.pth',
    model_name='scrfd_34g',
    device='cuda',
    use_tracking=True,
    use_smoothing=True,
)

# Process video file
stats = detector.process_video(
    source='input.mp4',
    output_path='output.mp4',
    show=True,
)
# → {total_frames: 1200, avg_fps: 75.3, avg_faces_per_frame: 4.2}

Detect faces in a single image

python

from facedet import build_detector
import cv2, torch

model = build_detector('scrfd_34g').cuda().eval()
# Load checkpoint...

img = cv2.imread('photo.jpg')
# Preprocess... (see scripts/evaluate.py for full example)
results = model(tensor)
# → [{'boxes': tensor([...]), 'scores': tensor([...])}]

Real-time webcam

bash

python scripts/detect_video.py \
    --model scrfd_2.5g \
    --checkpoint checkpoints/scrfd_2.5g_best.pth \
    --input 0 --show

Training

Dataset Setup

Download WIDER FACE and arrange:

data/wider_face/
├── WIDER_train/images/
├── WIDER_val/images/
├── wider_face_split/
│   ├── wider_face_train_bbx_gt.txt
│   └── wider_face_val_bbx_gt.txt
└── retinaface_gt/  (optional, for landmark training)
    ├── train/label.txt
    └── val/label.txt

Training Commands

bash

# Single GPU — SCRFD-34G (flagship)
python scripts/train.py \
    --model scrfd_34g \
    --data-root data/wider_face \
    --epochs 640 \
    --batch-size 8 \
    --lr 0.01

# Multi-GPU — 4× V100
torchrun --nproc_per_node=4 scripts/train.py \
    --model scrfd_34g \
    --data-root data/wider_face \
    --epochs 640 \
    --batch-size 8 \
    --lr 0.01

# Real-time variant
python scripts/train.py \
    --model scrfd_2.5g \
    --data-root data/wider_face \
    --epochs 640 \
    --batch-size 16 \
    --lr 0.02

Training Recipe (from SCRFD paper)

Parameter	Value	Rationale
Optimizer	SGD (m=0.9, wd=5e-4)	Standard for detection
Base LR	0.01 (8 imgs/GPU)	Linear scaling rule
LR Schedule	MultiStep [440, 544] ×0.1	Long training, late decay
Warmup	3 epochs, linear from 1e-5	Prevent early divergence
Total Epochs	640	Train from scratch
Input Size	640×640	Random crop from larger
Crop Scales	[0.3, 0.45, ..., 2.0]	Sample Redistribution
Augmentation	Crop + flip + photometric + robustness	See data/augmentations.py
Normalization	GroupNorm	Batch-size independent
Matching	ATSS (k=9)	Adaptive thresholds
Cls Loss	GFL (β=2)	Joint quality score
Reg Loss	DIoU	Better for tiny faces
Mixed Precision	✓	2× training speed

Evaluation

bash

python scripts/evaluate.py \
    --model scrfd_34g \
    --checkpoint checkpoints/scrfd_34g_best.pth \
    --data-root data/wider_face \
    --output-dir results/scrfd_34g \
    --benchmark

Generates:

WiderFace Easy/Medium/Hard AP scores
Predictions in WiderFace submission format
Speed benchmark table (320/480/640/960px)

Deployment

ONNX Export

bash

python scripts/export.py \
    --model scrfd_34g \
    --checkpoint checkpoints/scrfd_34g_best.pth \
    --output deploy/scrfd_34g.onnx \
    --input-size 640

TensorRT (FP16)

bash

trtexec --onnx=deploy/scrfd_34g.onnx \
        --saveEngine=deploy/scrfd_34g_fp16.engine \
        --fp16 --workspace=4096

Expected Deployment Speedups

Model	PyTorch FP32	ONNX RT	TensorRT FP16	TensorRT INT8
SCRFD-34G	~80 FPS	~100 FPS	~200 FPS	~350 FPS
SCRFD-2.5G	~400 FPS	~500 FPS	~800 FPS	~1200 FPS
SCRFD-0.5G	~1000 FPS	~1200 FPS	~2000 FPS	~3000 FPS

PyTorch Quantization (CPU)

python

from facedet.deploy import quantize_model
quantized = quantize_model(model, method='dynamic')

Ablation Studies

Configured in configs/ablations.yaml. Each ablation isolates one variable:

Ablation	Variables	Expected Finding
Sample Redistribution	Crop scales [0.3–1.0] vs [0.3–2.0]	+5-8% Hard AP from large crops
Loss Functions	GFL+DIoU vs Focal+SmoothL1	GFL: +1-2% from quality scores
Matching Strategy	ATSS(k=9) vs IoU(0.35) vs IoU(0.5)	ATSS: best for mixed scales
Robustness Augmentation	None / blur / JPEG / all	All: +1-3% on degraded inputs
Normalization	GroupNorm vs BatchNorm	GN: stable at batch<8
Input Resolution	320 / 480 / 640 / 960	960: +5-10% Hard AP, 4× slower
Landmarks	With/without 5-point landmarks	+~1% Hard AP (RetinaFace finding)
Tracker Config	None / conservative / aggressive	Aggressive: more tracks, more FP

Handling Challenging Conditions

Tiny Faces (<16px)

Sample Redistribution (crop scale up to 2.0×) generates small face training samples
Stride-8 feature maps with anchors [16, 32]px
Higher inference resolution (960px) trades speed for +5-10% small face recall
ATSS matching gives tiny faces lower IoU thresholds automatically

Blur / Motion Blur

Training augmentation: Gaussian blur σ∈[0.5, 3.0] applied with p=0.2
Model learns blur-invariant features
ByteTrack Kalman filter predicts through blurred frames

Occlusion

Random erasing (Cutout) during training simulates partial occlusion
ATSS assigns multiple anchors per GT → partial detection still gets signal
ByteTrack 2nd-stage matching recovers occluded faces with low-confidence detections

Poor Lighting

Gamma darkening augmentation (γ∈[1.5, 3.0]) simulates low-light
Photometric distortion (brightness, contrast jitter)
For extreme cases: pair with CLAHE preprocessing

Compression Artifacts

JPEG quality degradation (Q=20-80) during training
No published method addresses this — our augmentation is novel for face detection

Temporal Stability

ByteTrack: stable track IDs across frames, handles occlusion
Kalman filter: smooth trajectory prediction
Temporal EMA: adaptive smoothing eliminates box jitter
Keyframe strategy: full detection every N frames, tracker-only in between

Repository Structure

facedet/
├── README.md                 # This file
├── setup.py                  # Package installation
├── requirements.txt          # Dependencies
│
├── models/                   # Model architectures
│   ├── backbone.py           # NAS-searched ResNet backbones
│   ├── neck.py               # PAFPN feature pyramid
│   ├── head.py               # Shared detection head (cls/reg/lmk)
│   ├── anchor.py             # Anchor generation + ATSS matching
│   ├── losses.py             # GFL, DIoU, Focal, Landmark losses
│   └── detector.py           # Full SCRFD detector (train + inference)
│
├── data/                     # Data pipeline
│   ├── widerface.py          # WiderFace dataset loader
│   ├── augmentations.py      # Training/val/robustness augmentations
│   └── dataloader.py         # DataLoader builders
│
├── engine/                   # Video inference engine
│   ├── video_detector.py     # End-to-end video processing
│   ├── tracker.py            # ByteTrack face tracker
│   └── temporal.py           # Temporal EMA smoother
│
├── evaluation/               # Evaluation suite
│   ├── widerface_eval.py     # WiderFace protocol (Easy/Med/Hard AP)
│   ├── speed_benchmark.py    # Latency/throughput benchmarks
│   └── metrics.py            # Core metrics (AP, IoU, recall)
│
├── deploy/                   # Deployment
│   ├── export_onnx.py        # ONNX export + verification
│   └── optimize.py           # Quantization, TensorRT guide
│
├── configs/                  # Configuration files
│   ├── scrfd_34g.yaml        # Flagship (quality)
│   ├── scrfd_10g.yaml        # Balanced
│   ├── scrfd_2.5g.yaml       # Real-time
│   ├── scrfd_0.5g.yaml       # Mobile
│   └── ablations.yaml        # Ablation study configs
│
├── scripts/                  # Entry points
│   ├── train.py              # Training (single/multi-GPU)
│   ├── evaluate.py           # WiderFace evaluation + speed bench
│   ├── detect_video.py       # Video inference CLI
│   └── export.py             # ONNX export CLI
│
└── utils/                    # Helpers
    ├── visualization.py      # Drawing utilities
    └── io.py                 # Checkpoint I/O

References

SCRFD: Guo et al., "Sample and Computation Redistribution for Efficient Face Detection", arXiv:2105.04714 (2021)
RetinaFace: Deng et al., "RetinaFace: Single-shot Multi-level Face Localisation in the Wild", arXiv:1905.00641 (2019)
TinaFace: Zhu et al., "TinaFace: Strong but Simple Baseline for Face Detection", arXiv:2011.13183 (2020)
ByteTrack: Zhang et al., "ByteTrack: Multi-Object Tracking by Associating Every Detection Box", ECCV 2022
ATSS: Zhang et al., "Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection", CVPR 2020
GFL: Li et al., "Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection", NeurIPS 2020
DIoU: Zheng et al., "Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression", AAAI 2020
ASFD: Zhang et al., "ASFD: Automatic and Scalable Face Detector", arXiv:2201.10781 (2022)
DSFD: Li et al., "DSFD: Dual Shot Face Detector", CVPR 2019
WiderFace: Yang et al., "WIDER FACE: A Face Detection Benchmark", CVPR 2016

License

Apache 2.0

cledouxluma/facedet

FaceDet — Production Face Detection for Video

Architecture Survey & Design Decisions

Ranked Candidate Models (WiderFace Hard AP)

Why SCRFD?

Key Technical Insights From Literature

Model Zoo

Architecture

Video Pipeline

Quick Start

Installation

Detect faces in a video

Detect faces in a single image

Real-time webcam

Training

Dataset Setup

Training Commands

Training Recipe (from SCRFD paper)

Evaluation

Deployment

ONNX Export

TensorRT (FP16)

Expected Deployment Speedups

PyTorch Quantization (CPU)

Ablation Studies

Handling Challenging Conditions

Tiny Faces (<16px)

Blur / Motion Blur

Occlusion

Poor Lighting

Compression Artifacts

Temporal Stability

Repository Structure

References

License

cledouxluma/
facedet