tensorflow / tensorflow/models
How to improve the performance of the object detection example in the Android sample?
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Description
I trained ssd_mobilenet_v2 object detection model by https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/running_on_mobile_tf2.md and https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf2_detection_zoo.md
and convert to tflite to run on android app
https://github.com/tensorflow/examples/tree/master/lite/examples/object_detection.
The object detection processing time is approximately 30ms above.
I have used nnapi to increase speed, I hope to achieve an object detection time of less than 30ms . What methods can I use to further improve the detection speed?
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start by reviewing the linked TensorFlow object-detection documentation and the lite/examples/object_detection Android sample. Profile the existing NNAPI-backed inference path to identify the remaining processing cost, then determine which supported changes could meet the requested under-30ms target and validate the result on the same device and model.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- android, tensorflow
- Domain
- machine-learning, mobile-dev
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100