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---
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layout: forward
target: https://developers.google.com/mediapipe/solutions/guide#legacy
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title: Models and Model Cards
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parent: MediaPipe Legacy Solutions
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nav_order: 30
---
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# MediaPipe Models and Model Cards
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1. TOC
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---
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**Attention:** *Thank you for your interest in MediaPipe Solutions.
We have ended support for
[these MediaPipe Legacy Solutions ](https://developers.google.com/mediapipe/solutions/guide#legacy )
as of March 1, 2023. All other
[MediaPipe Legacy Solutions will be upgraded ](https://developers.google.com/mediapipe/solutions/guide#legacy )
to a new MediaPipe Solution. The code repository and prebuilt binaries for all
MediaPipe Legacy Solutions will continue to be provided on an as-is basis.
We encourage you to check out the new MediaPipe Solutions at:
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[https://developers.google.com/mediapipe/solutions ](https://developers.google.com/mediapipe/solutions )*
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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 ),
[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
[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.
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### [Face Mesh](https://google.github.io/mediapipe/solutions/face_mesh)
* 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 ),
[Model card (w/ attention) ](https://mediapipe.page.link/attentionmesh-mc )
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### [Iris](https://google.github.io/mediapipe/solutions/iris)
* 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 )
### [Hands](https://google.github.io/mediapipe/solutions/hands)
* Palm detection model:
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[TFLite model (lite) ](https://storage.googleapis.com/mediapipe-assets/palm_detection_lite.tflite ),
[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 )
* Hand landmark model:
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[TFLite model (lite) ](https://storage.googleapis.com/mediapipe-assets/hand_landmark_lite.tflite ),
[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)
* 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 ),
[TFLite model (full) ](https://storage.googleapis.com/mediapipe-assets/pose_landmark_full.tflite ),
[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)
* 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 )
* [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 )
### [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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* [TensorFlow model ](https://storage.googleapis.com/mediapipe-assets/object_detection_saved_model/archive.zip )
* [Model information ](https://storage.googleapis.com/mediapipe-assets/object_detection_saved_model/README.md )
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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 )
* [TFLite model for chairs ](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_chair.tflite )
* [TFLite model for cameras ](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_camera.tflite )
* [TFLite model for cups ](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_cup.tflite )
* [Single-stage TFLite model for shoes ](https://storage.googleapis.com/mediapipe-assets/object_detection_3d_sneakers_1stage.tflite )
* [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 )
* [TFLite model for up to 400 keypoints ](https://storage.googleapis.com/mediapipe-assets/knift_float_400.tflite )
* [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 )
