114 lines
6.1 KiB
Markdown
114 lines
6.1 KiB
Markdown
---
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layout: default
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title: Models and Model Cards
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parent: Solutions
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nav_order: 30
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---
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# MediaPipe Models and Model Cards
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{: .no_toc }
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1. TOC
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{:toc}
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---
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### [Face Detection](https://google.github.io/mediapipe/solutions/face_detection)
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* Short-range model (best for faces within 2 meters from the camera):
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[TFLite model](https://storage.googleapis.com/mediapipe-assets/face_detection_short_range.tflite),
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[TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite),
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[Model card](https://mediapipe.page.link/blazeface-mc)
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* Full-range model (dense, best for faces within 5 meters from the camera):
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[TFLite model](https://storage.googleapis.com/mediapipe-assets/face_detection_full_range.tflite),
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[Model card](https://mediapipe.page.link/blazeface-back-mc)
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* Full-range model (sparse, best for faces within 5 meters from the camera):
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[TFLite model](https://storage.googleapis.com/mediapipe-assets/face_detection_full_range_sparse.tflite),
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[Model card](https://mediapipe.page.link/blazeface-back-sparse-mc)
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Full-range dense and sparse models have the same quality in terms of
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[F-score](https://en.wikipedia.org/wiki/F-score) however differ in underlying
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metrics. The dense model is slightly better in
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[Recall](https://en.wikipedia.org/wiki/Precision_and_recall) whereas the sparse
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model outperforms the dense one in
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[Precision](https://en.wikipedia.org/wiki/Precision_and_recall). Speed-wise
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sparse model is ~30% faster when executing on CPU via
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[XNNPACK](https://github.com/google/XNNPACK) whereas on GPU the models
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demonstrate comparable latencies. Depending on your application, you may prefer
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one over the other.
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### [Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)
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* Face landmark model:
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[TFLite model](https://storage.googleapis.com/mediapipe-assets/face_landmark.tflite),
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[TF.js model](https://tfhub.dev/mediapipe/facemesh/1)
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* Face landmark model w/ attention (aka Attention Mesh):
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[TFLite model](https://storage.googleapis.com/mediapipe-assets/face_landmark_with_attention.tflite)
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* [Model card](https://mediapipe.page.link/facemesh-mc),
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[Model card (w/ attention)](https://mediapipe.page.link/attentionmesh-mc)
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### [Iris](https://google.github.io/mediapipe/solutions/iris)
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* Iris landmark model:
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[TFLite model](https://storage.googleapis.com/mediapipe-assets/iris_landmark.tflite)
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* [Model card](https://mediapipe.page.link/iris-mc)
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### [Hands](https://google.github.io/mediapipe/solutions/hands)
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* Palm detection model:
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[TFLite model (lite)](https://storage.googleapis.com/mediapipe-assets/palm_detection_lite.tflite),
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[TFLite model (full)](https://storage.googleapis.com/mediapipe-assets/palm_detection_full.tflite),
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[TF.js model](https://tfhub.dev/mediapipe/handdetector/1)
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* Hand landmark model:
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[TFLite model (lite)](https://storage.googleapis.com/mediapipe-assets/hand_landmark_lite.tflite),
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[TFLite model (full)](https://storage.googleapis.com/mediapipe-assets/hand_landmark_full.tflite),
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[TF.js model](https://tfhub.dev/mediapipe/handskeleton/1)
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* [Model card](https://mediapipe.page.link/handmc)
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### [Pose](https://google.github.io/mediapipe/solutions/pose)
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* Pose detection model:
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[TFLite model](https://storage.googleapis.com/mediapipe-assets/pose_detection.tflite)
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* Pose landmark model:
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[TFLite model (lite)](https://storage.googleapis.com/mediapipe-assets/pose_landmark_lite.tflite),
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[TFLite model (full)](https://storage.googleapis.com/mediapipe-assets/pose_landmark_full.tflite),
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[TFLite model (heavy)](https://storage.googleapis.com/mediapipe-assets/pose_landmark_heavy.tflite)
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* [Model card](https://mediapipe.page.link/blazepose-mc)
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### [Holistic](https://google.github.io/mediapipe/solutions/holistic)
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* Hand recrop model:
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[TFLite model](https://storage.googleapis.com/mediapipe-assets/hand_recrop.tflite)
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### [Selfie Segmentation](https://google.github.io/mediapipe/solutions/selfie_segmentation)
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* [TFLite model (general)](https://storage.googleapis.com/mediapipe-assets/selfie_segmentation.tflite)
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* [TFLite model (landscape)](https://storage.googleapis.com/mediapipe-assets/selfie_segmentation_landscape.tflite)
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* [Model card](https://mediapipe.page.link/selfiesegmentation-mc)
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### [Hair Segmentation](https://google.github.io/mediapipe/solutions/hair_segmentation)
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* [TFLite model](https://storage.googleapis.com/mediapipe-assets/hair_segmentation.tflite)
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* [Model card](https://mediapipe.page.link/hairsegmentation-mc)
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### [Object Detection](https://google.github.io/mediapipe/solutions/object_detection)
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* [TFLite model](https://storage.googleapis.com/mediapipe-assets/ssdlite_object_detection.tflite)
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* [TFLite model quantized for EdgeTPU/Coral](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/models/object-detector-quantized_edgetpu.tflite)
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### [Objectron](https://google.github.io/mediapipe/solutions/objectron)
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* [TFLite model for shoes](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_sneakers.tflite)
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* [TFLite model for chairs](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_chair.tflite)
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* [TFLite model for cameras](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_camera.tflite)
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* [TFLite model for cups](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_cup.tflite)
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* [Single-stage TFLite model for shoes](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_sneakers_1stage.tflite)
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* [Single-stage TFLite model for chairs](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_chair_1stage.tflite)
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* [Model card](https://mediapipe.page.link/objectron-mc)
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### [KNIFT](https://google.github.io/mediapipe/solutions/knift)
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* [TFLite model for up to 200 keypoints](https://storage.googleapis.com/mediapipe-assets/knift_float.tflite)
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* [TFLite model for up to 400 keypoints](https://storage.googleapis.com/mediapipe-assets/knift_float_400.tflite)
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* [TFLite model for up to 1000 keypoints](https://storage.googleapis.com/mediapipe-assets/knift_float_1k.tflite)
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* [Model card](https://mediapipe.page.link/knift-mc)
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