90 lines
4.2 KiB
Markdown
90 lines
4.2 KiB
Markdown
---
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layout: forward
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target: https://developers.google.com/mediapipe/solutions/vision/object_detector
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title: Object Detection
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parent: Solutions
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nav_order: 9
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---
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# MediaPipe Object Detection
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{: .no_toc }
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<details close markdown="block">
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<summary>
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Table of contents
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</summary>
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{: .text-delta }
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1. TOC
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{:toc}
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</details>
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---
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**Attention:** *Thank you for your interest in MediaPipe Solutions.
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As of March 1, 2023, this solution was upgraded to a new MediaPipe
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Solution. For more information, see the
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[MediaPipe Solutions](https://developers.google.com/mediapipe/solutions/vision/object_detector)
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site.*
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----
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### TensorFlow model
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The model is trained on [MSCOCO 2014](http://cocodataset.org) dataset using [TensorFlow Object Detection API](https://github.com/tensorflow/models/tree/master/research/object_detection). It is a MobileNetV2-based SSD model with 0.5 depth multiplier. Detailed training configuration is in the provided `pipeline.config`. The model is a relatively compact model which has `0.171 mAP` to achieve real-time performance on mobile devices. You can compare it with other models from the [TensorFlow detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md).
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### TFLite model
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The TFLite model is converted from the TensorFlow above. The steps needed to convert the model are similar to [this tutorial](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193) with minor modifications. Assuming now we have a trained TensorFlow model which includes the checkpoint files and the training configuration file, for example the files provided in this repo:
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* `model.ckpt.index`
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* `model.ckpt.meta`
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* `model.ckpt.data-00000-of-00001`
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* `pipeline.config`
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Make sure you have installed these [python libraries](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1.md). Then to get the frozen graph, run the `export_tflite_ssd_graph.py` script from the `models/research` directory with this command:
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```bash
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$ PATH_TO_MODEL=path/to/the/model
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$ bazel run object_detection:export_tflite_ssd_graph -- \
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--pipeline_config_path ${PATH_TO_MODEL}/pipeline.config \
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--trained_checkpoint_prefix ${PATH_TO_MODEL}/model.ckpt \
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--output_directory ${PATH_TO_MODEL} \
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--add_postprocessing_op=False
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```
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The exported model contains two files:
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* `tflite_graph.pb`
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* `tflite_graph.pbtxt`
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The difference between this step and the one in [the tutorial](https://medium.com/tensorflow/training-and-serving-a-realtime-mobile-object-detector-in-30-minutes-with-cloud-tpus-b78971cf1193) is that we set `add_postprocessing_op` to False. In MediaPipe, we have provided all the calculators needed for post-processing such that we can exclude the custom TFLite ops for post-processing in the original graph, e.g., non-maximum suppression. This enables the flexibility to integrate with different post-processing algorithms and implementations.
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Optional: You can install and use the [graph tool](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/tools/graph_transforms) to inspect the input/output of the exported model:
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```bash
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$ bazel run graph_transforms:summarize_graph -- \
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--in_graph=${PATH_TO_MODEL}/tflite_graph.pb
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```
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You should be able to see the input image size of the model is 320x320 and the outputs of the model are:
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* `raw_outputs/box_encodings`
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* `raw_outputs/class_predictions`
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The last step is to convert the model to TFLite. You can look at [this guide](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/r1/convert/cmdline_examples.md) for more detail. For this example, you just need to run:
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```bash
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$ tflite_convert -- \
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--graph_def_file=${PATH_TO_MODEL}/tflite_graph.pb \
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--output_file=${PATH_TO_MODEL}/model.tflite \
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--input_format=TENSORFLOW_GRAPHDEF \
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--output_format=TFLITE \
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--inference_type=FLOAT \
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--input_shapes=1,320,320,3 \
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--input_arrays=normalized_input_image_tensor \
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--output_arrays=raw_outputs/box_encodings,raw_outputs/class_predictions
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```
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Now you have the TFLite model `model.tflite` ready to use with MediaPipe Object Detection graphs. Please see the examples for more detail.
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