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MediaPipe Team
2021-06-24 17:55:26 -04:00
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### [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)
* Face detection model for front-facing/selfie camera:
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_front.tflite),
* Short-range model (best for faces within 2 meters from the camera):
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_short_range.tflite),
[TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite),
[Model card](https://mediapipe.page.link/blazeface-mc)
* Face detection model for back-facing camera:
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_back.tflite),
* Full-range model (dense, best for faces within 5 meters from the camera):
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_full_range.tflite),
[Model card](https://mediapipe.page.link/blazeface-back-mc)
* Face detection model for back-facing camera (sparse):
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_back_sparse.tflite),
* Full-range model (sparse, best for faces within 5 meters from the camera):
[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/modules/face_detection/face_detection_full_range_sparse.tflite),
[Model card](https://mediapipe.page.link/blazeface-back-sparse-mc)
Full-range dense and sparse models have the same quality in terms of
[F-score](https://en.wikipedia.org/wiki/F-score) however differ in underlying
metrics. The dense model is slightly better in
[Recall](https://en.wikipedia.org/wiki/Precision_and_recall) whereas the sparse
model outperforms the dense one in
[Precision](https://en.wikipedia.org/wiki/Precision_and_recall). Speed-wise
sparse model is ~30% faster when executing on CPU via
[XNNPACK](https://github.com/google/XNNPACK) whereas on GPU the models
demonstrate comparable latencies. Depending on your application, you may prefer
one over the other.
### [Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)