Tiny-Face: Ultra-Lightweight Face Detection for Edge Devices
Tiny-Face is a compact face detection project focused on mobile and edge environments. It compares three small detector variants: SlimFace, RFB, and a compact RetinaFace model. See the project on github.com/yakhyo/tiny-face-pytorch.
The project is based on the RetinaFace-style detection pipeline but reduces the model size for low-resource inference.
Model Variants
| Model | Parameters | Size | Input |
|---|---|---|---|
| SlimFace | 0.343M | 1.4 MB | 640x640 |
| RFB | 0.359M | 1.5 MB | 640x640 |
| RetinaFace | 0.426M | 1.8 MB | 640x640 |
All three published models have PyTorch and ONNX weights in the repository release.
WIDER FACE Results
Multi-scale Image Size
| Model | Easy | Medium | Hard |
|---|---|---|---|
| SlimFace | 79.50% | 79.40% | 68.36% |
| RFB | 80.49% | 81.51% | 75.73% |
| RetinaFace | 87.69% | 86.39% | 80.21% |
Original Image Size
| Model | Easy | Medium | Hard |
|---|---|---|---|
| SlimFace | 87.10% | 84.36% | 67.38% |
| RFB | 87.09% | 84.61% | 69.22% |
| RetinaFace | 90.26% | 87.48% | 72.85% |
The compact RetinaFace variant is the strongest model in both tables. SlimFace and RFB are smaller alternatives for stricter model-size constraints.
Large Selfie Test
The README includes a crowded selfie example and reports how many faces each model detects:
| Model | Faces detected |
|---|---|
| RetinaFace | 459 |
| RFB | 430 |
| SlimFace | 384 |
This example shows the recall tradeoff clearly. Smaller models are useful on constrained devices, but crowded images make missed detections more likely.
What the Repository Contains
The repository includes WIDER FACE training and evaluation code, pretrained PyTorch weights, ONNX weights, and inference code for the three detector variants.
It is useful when model size is a first-order constraint and a larger detector is too expensive for the target device.