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---
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layout: default
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title: Object Detection
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parent: Solutions
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nav_order: 5
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---
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# MediaPipe Object Detection
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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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## Example Apps
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Note: To visualize a graph, copy the graph and paste it into
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[MediaPipe Visualizer](https://viz.mediapipe.dev/). For more information on how
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to visualize its associated subgraphs, please see
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[visualizer documentation](../visualizer.md).
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### Mobile
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Please first see general instructions for
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[Android](../getting_started/building_examples.md#android) and
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[iOS](../getting_started/building_examples.md#ios) on how to build MediaPipe examples.
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#### GPU Pipeline
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* Graph:
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[`mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_gpu.pbtxt)
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* Android target:
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[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1di2ywCA_acf3y5rIcJHngWHAUNsUHAGz)
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[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectiongpu:objectdetectiongpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectiongpu/BUILD)
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* iOS target:
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[`mediapipe/examples/ios/objectdetectiongpu:ObjectDetectionGpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/objectdetectiongpu/BUILD)
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#### CPU Pipeline
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This is very similar to the [GPU pipeline](#gpu-pipeline) except that at the
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beginning and the end of the pipeline it performs GPU-to-CPU and CPU-to-GPU
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image transfer respectively. As a result, the rest of graph, which shares the
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same configuration as the GPU pipeline, runs entirely on CPU.
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* Graph:
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[`mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt))
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* Android target:
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[(or download prebuilt ARM64 APK)](https://drive.google.com/open?id=1eRBK6V5Qd1LCRwexitR2OXgrBBXbOfZ5)
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[`mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectioncpu:objectdetectioncpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/android/src/java/com/google/mediapipe/apps/objectdetectioncpu/BUILD)
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* iOS target:
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[`mediapipe/examples/ios/objectdetectioncpu:ObjectDetectionCpuApp`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/ios/objectdetectioncpu/BUILD)
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### Desktop
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#### Live Camera Input
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Please first see general instructions for
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[desktop](../getting_started/building_examples.md#desktop) on how to build MediaPipe examples.
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* Graph:
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[`mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt`](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_desktop_live.pbtxt)
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* Target:
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[`mediapipe/examples/desktop/object_detection:object_detection_cpu`](https://github.com/google/mediapipe/tree/master/mediapipe/examples/desktop/object_detection/BUILD)
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#### Video File Input
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* With a TFLite Model
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This uses the same
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[TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/ssdlite_object_detection.tflite)
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(see also
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[model info](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model/README.md))
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as in [Live Camera Input](#live-camera-input) above. The pipeline is
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implemented in this
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[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt),
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which differs from the live-camera-input CPU-based pipeline
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[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt)
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simply by the additional `OpenCvVideoDecoderCalculator` and
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`OpenCvVideoEncoderCalculator` at the beginning and the end of the graph
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respectively.
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To build the application, run:
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```bash
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bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 mediapipe/examples/desktop/object_detection:object_detection_tflite
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```
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To run the application, replace `<input video path>` and `<output video
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path>` in the command below with your own paths:
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Tip: You can find a test video available in
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`mediapipe/examples/desktop/object_detection`.
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```
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GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tflite \
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--calculator_graph_config_file=mediapipe/graphs/object_detection/object_detection_desktop_tflite_graph.pbtxt \
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--input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>
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```
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* With a TensorFlow Model
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This uses the
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[TensorFlow model](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model)
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( see also
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[model info](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model/README.md)),
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and the pipeline is implemented in this
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[graph](https://github.com/google/mediapipe/tree/master/mediapipe/graphs/object_detection/object_detection_mobile_cpu.pbtxt).
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Note: The following runs TensorFlow inference on CPU. If you would like to
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run inference on GPU (Linux only), please follow
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[TensorFlow CUDA Support and Setup on Linux Desktop](gpu.md#tensorflow-cuda-support-and-setup-on-linux-desktop)
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instead.
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To build the TensorFlow CPU inference example on desktop, run:
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Note: This command also builds TensorFlow targets from scratch, and it may
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take a long time (e.g., up to 30 mins) for the first time.
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```bash
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bazel build -c opt --define MEDIAPIPE_DISABLE_GPU=1 --define no_aws_support=true --linkopt=-s \
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mediapipe/examples/desktop/object_detection:object_detection_tensorflow
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```
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To run the application, replace `<input video path>` and `<output video
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path>` in the command below with your own paths:
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Tip: You can find a test video available in
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`mediapipe/examples/desktop/object_detection`.
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```bash
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GLOG_logtostderr=1 bazel-bin/mediapipe/examples/desktop/object_detection/object_detection_tflite \
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--calculator_graph_config_file=mediapipe/graphs/object_detection/object_detection_desktop_tensorflow_graph.pbtxt \
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--input_side_packets=input_video_path=<input video path>,output_video_path=<output video path>
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```
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### Coral
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Please refer to
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[these instructions](https://github.com/google/mediapipe/tree/master/mediapipe/examples/coral/README.md)
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to cross-compile and run MediaPipe examples on the
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[Coral Dev Board](https://coral.ai/products/dev-board).
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## Resources
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* [TFLite model](https://github.com/google/mediapipe/tree/master/mediapipe/models/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://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model)
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* [Model information](https://github.com/google/mediapipe/tree/master/mediapipe/models/object_detection_saved_model/README.md)
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