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MediaPipe Team
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### Hello World! on Android
[Hello World! on Android](./hello_world_android.md) should be the first mobile
example users go through in detail. It teaches the following:
Android example users go through in detail. It teaches the following:
* Introduction of a simple MediaPipe graph running on mobile GPUs for
[Sobel edge detection].
[Sobel edge detection](https://en.wikipedia.org/wiki/Sobel_operator).
* Building a simple baseline Android application that displays "Hello World!".
* Adding camera preview support into the baseline application using the
Android [CameraX] API.
* Incorporating the Sobel edge detection graph to process the live camera
preview and display the processed video in real-time.
### Object Detection with GPU on Android
### Hello World! on iOS
[Object Detection on GPU on Android](./object_detection_android_gpu.md)
illustrates how to use MediaPipe with a TFLite model for object detection in a
GPU-accelerated pipeline.
[Hello World! on iOS](./hello_world_ios.md) is the iOS version of Sobel edge
detection example
### Object Detection with CPU on Android
### Object Detection with GPU
[Object Detection on CPU on Android](./object_detection_android_cpu.md)
illustrates using the same TFLite model in a CPU-based pipeline. This example
highlights how graphs can be easily adapted to run on CPU v.s. GPU.
### Face Detection on Android
[Face Detection on Android](./face_detection_android_gpu.md) illustrates how to
use MediaPipe with a TFLite model for face detection in a GPU-accelerated
[Object Detection with GPU](./object_detection_mobile_gpu.md) illustrates how to
use MediaPipe with a TFLite model for object detection in a GPU-accelerated
pipeline.
* The selfie face detection TFLite model is based on
["BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs"](https://sites.google.com/view/perception-cv4arvr/blazeface).
* [Model card](https://sites.google.com/corp/view/perception-cv4arvr/blazeface#h.p_21ojPZDx3cqq).
* [Android](./object_detection_mobile_gpu.md#android)
* [iOS](./object_detection_mobile_gpu.md#ios)
### Hair Segmentation on Android
### Object Detection with CPU
[Hair Segmentation on Android](./hair_segmentation_android_gpu.md) illustrates
how to use MediaPipe with a TFLite model for hair segmentation in a
GPU-accelerated pipeline.
[Object Detection with CPU](./object_detection_mobile_cpu.md) illustrates using
the same TFLite model in a CPU-based pipeline. This example highlights how
graphs can be easily adapted to run on CPU v.s. GPU.
* The selfie hair segmentation TFLite model is based on
["Real-time Hair segmentation and recoloring on Mobile GPUs"](https://sites.google.com/view/perception-cv4arvr/hair-segmentation).
* [Model card](https://sites.google.com/corp/view/perception-cv4arvr/hair-segmentation#h.p_NimuO7PgHxlY).
### Face Detection with GPU
[Face Detection with GPU](./face_detection_mobile_gpu.md) illustrates how to use
MediaPipe with a TFLite model for face detection in a GPU-accelerated pipeline.
The selfie face detection TFLite model is based on
["BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs"](https://sites.google.com/view/perception-cv4arvr/blazeface).
[Model card](https://sites.google.com/corp/view/perception-cv4arvr/blazeface#h.p_21ojPZDx3cqq).
* [Android](./face_detection_mobile_gpu.md#android)
* [iOS](./face_detection_mobile_gpu.md#ios)
### Hand Detection with GPU
[Hand Detection with GPU](./hand_detection_mobile_gpu.md) illustrates how to use
MediaPipe with a TFLite model for hand detection in a GPU-accelerated pipeline.
* [Android](./hand_detection_mobile_gpu.md#android)
* [iOS](./hand_detection_mobile_gpu.md#ios)
### Hand Tracking with GPU
[Hand Tracking with GPU](./hand_tracking_mobile_gpu.md) illustrates how to use
MediaPipe with a TFLite model for hand tracking in a GPU-accelerated pipeline.
* [Android](./hand_tracking_mobile_gpu.md#android)
* [iOS](./hand_tracking_mobile_gpu.md#ios)
### Hair Segmentation with GPU
[Hair Segmentation on GPU](./hair_segmentation_mobile_gpu.md) illustrates how to
use MediaPipe with a TFLite model for hair segmentation in a GPU-accelerated
pipeline. The selfie hair segmentation TFLite model is based on
["Real-time Hair segmentation and recoloring on Mobile GPUs"](https://sites.google.com/view/perception-cv4arvr/hair-segmentation).
[Model card](https://sites.google.com/corp/view/perception-cv4arvr/hair-segmentation#h.p_NimuO7PgHxlY).
* [Android](./hair_segmentation_mobile_gpu.md#android)
## Desktop