COSCUP2020
入門
TR212
歡迎來到 https://hackmd.io/@coscup/2020 共筆
請從這裡開始
GDG 攤位在 TR312 有貼紙可以拿喔!
涵蓋google技術,開放源碼相關內容技術,一整天議程歡迎聆聽。
Why tf-lite?
TFlite in Android and IOS device.
Mobile -> IOT device
“There is an old network saying: Bandwidth problems can be cured with money. Latency problems are harder because the speed of light is fixed — you can't bribe God.” – David D. Clark
• interpreter: loads and runs a model on various hardware
• converter: converts TF models to a TFLite specific format to be used by the interpreter.
See https://www.tensorflow.org/lite/guide for more introduction materials
There is a good guide on how to load a model and do inference on devices using TFLite interpreter, in Java(Android), Swift(IOS), Objective-C, C++, and Python
• https://www.tensorflow.org/lite/guide/inference
TFLite metadata and TFLite Android code generator.
In TFLite metadata, there are three parts in the schema:
• Model information
• Input information
• Output information
• Feature
• Image
• Bounding box
Pack the associated files, e.g., label file(s)
Normalization and quantization parameters
With example at here, we can create a image classifier with
• image input
• label output
https://github.com/freedomtan/CameraxTFLite
Some new features: CoreML delegate and XNNPACK delegate.
“A TensorFlow Lite delegate is a way to delegate part or all of graph execution to another executor.”
Flashback: COSCUP 2019 slide
how NNAPI and GPU delegates work, https://www.slideshare.net/kstan2/tflite-nnapi-and-gpu-delegates
XNNPACK: Paper
Concluding remarks
The note is written by Simon
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