Nanodet trained models for Face Detection. For more details check out Nanodet repo. Models were trained with Google Colab servers (Single GPU)
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├── face # Face related object detection - OpenImages Dataset (20k train : 4k test)
│ ├── nanodet_m_1.0x_sgd # Cfg, checkpoints, ... for nanodet_m_1.0x @ 320x320
│ ├── nanodet_m_0.5x_sgd_416x416 # Cfg, checkpoints, ... for nanodet_m_0.5x @ 416x416
| ├── nd-efficientnet_lite0_more_aug_320x320 # Cfg, checkpoints, ... for efficientnet_lite0 @ 320x320
└── ...
Note: Current models are trained with 20k images (Human faces) of Open Images v6. All these faces satisfy: bbox_area/image_area > 0.03. So expect poor performance on those images which bbox occupy less than 3% of the image.
- ncnn optimized models
- EfficientNet-Lite0 model
- Yolo Fastest v2