--- layout: forward target: https://developers.google.com/mediapipe/solutions/guide#legacy title: Models and Model Cards parent: MediaPipe Legacy Solutions nav_order: 30 --- # MediaPipe Models and Model Cards {: .no_toc } 1. TOC {:toc} --- **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: [https://developers.google.com/mediapipe/solutions](https://developers.google.com/mediapipe/solutions)* ---- ### [Face Detection](https://google-ai-edge.github.io/mediapipe/solutions/face_detection) * Short-range model (best for faces within 2 meters from the camera): [TFLite model](https://storage.googleapis.com/mediapipe-assets/face_detection_short_range.tflite), [TFLite model quantized for EdgeTPU/Coral](https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/examples/coral/models/face-detector-quantized_edgetpu.tflite), [Model card](https://mediapipe.page.link/blazeface-mc) * Full-range model (dense, best for faces within 5 meters from the camera): [TFLite model](https://storage.googleapis.com/mediapipe-assets/face_detection_full_range.tflite), [Model card](https://mediapipe.page.link/blazeface-back-mc) * Full-range model (sparse, best for faces within 5 meters from the camera): [TFLite model](https://storage.googleapis.com/mediapipe-assets/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-ai-edge.github.io/mediapipe/solutions/face_mesh) * Face landmark model: [TFLite model](https://storage.googleapis.com/mediapipe-assets/face_landmark.tflite), [TF.js model](https://tfhub.dev/mediapipe/facemesh/1) * Face landmark model w/ attention (aka Attention Mesh): [TFLite model](https://storage.googleapis.com/mediapipe-assets/face_landmark_with_attention.tflite) * [Model card](https://mediapipe.page.link/facemesh-mc), [Model card (w/ attention)](https://mediapipe.page.link/attentionmesh-mc) ### [Iris](https://google-ai-edge.github.io/mediapipe/solutions/iris) * Iris landmark model: [TFLite model](https://storage.googleapis.com/mediapipe-assets/iris_landmark.tflite) * [Model card](https://mediapipe.page.link/iris-mc) ### [Hands](https://google-ai-edge.github.io/mediapipe/solutions/hands) * Palm detection model: [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), [TF.js model](https://tfhub.dev/mediapipe/handdetector/1) * Hand landmark model: [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), [TF.js model](https://tfhub.dev/mediapipe/handskeleton/1) * [Model card](https://mediapipe.page.link/handmc) ### [Pose](https://google-ai-edge.github.io/mediapipe/solutions/pose) * Pose detection model: [TFLite model](https://storage.googleapis.com/mediapipe-assets/pose_detection.tflite) * Pose landmark model: [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) * [Model card](https://mediapipe.page.link/blazepose-mc) ### [Holistic](https://google-ai-edge.github.io/mediapipe/solutions/holistic) * Hand recrop model: [TFLite model](https://storage.googleapis.com/mediapipe-assets/hand_recrop.tflite) ### [Selfie Segmentation](https://google-ai-edge.github.io/mediapipe/solutions/selfie_segmentation) * [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) * [Model card](https://mediapipe.page.link/selfiesegmentation-mc) ### [Hair Segmentation](https://google-ai-edge.github.io/mediapipe/solutions/hair_segmentation) * [TFLite model](https://storage.googleapis.com/mediapipe-assets/hair_segmentation.tflite) * [Model card](https://mediapipe.page.link/hairsegmentation-mc) ### [Object Detection](https://google-ai-edge.github.io/mediapipe/solutions/object_detection) * [TFLite model](https://storage.googleapis.com/mediapipe-assets/ssdlite_object_detection.tflite) * [TFLite model quantized for EdgeTPU/Coral](https://github.com/google-ai-edge/mediapipe/tree/master/mediapipe/examples/coral/models/object-detector-quantized_edgetpu.tflite) * [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) ### [Objectron](https://google-ai-edge.github.io/mediapipe/solutions/objectron) * [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) * [Model card](https://mediapipe.page.link/objectron-mc) ### [KNIFT](https://google-ai-edge.github.io/mediapipe/solutions/knift) * [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) * [Model card](https://mediapipe.page.link/knift-mc)