google-coral
Fast Custom Object Detection Model
Description
When will we ever be able to quantize a custom-trained Tensorflow model and make it as fast as the pretrained models that are supplied by Google Coral? All the resources provided by Google Coral are severely lacking with FPS under 10 and over 200 ms of delay. I have been struggling with this a while.
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Issue Type
Feature Request
Operating System
Mendel Linux
Coral Device
Dev Board
Other Devices
No response
Programming Language
Python 3.7
Relevant Log Output
No response
Hello @vyang2968 can you please share what type of model network you are trying to train and the model input size. Thanks!
@hjonnala, so far I have worked with EfficientDet-Lite and SSDLite. I have also simply trained SSD MobileNet V2 FPNLite 320x320 with the Tensorflow 2 Object Detection API and attempted to quantize it, but it has failed thus far. Preferably, I would like to use TF2 OD API and get a quantized model that I can use on the EdgeTPU, but that support does not look like it is coming anytime soon. As of now, I am working with SSD MobileNet V1 which looks promising, although it is a bit confusing. P