xFlynnRider

3 ms·

1. 1.▲
   by xFlynnRider 5y ago

   What model is this trained/fine-tuned on? What dataset have you used? Managed or on-premises?
2. 2.▲
   by xFlynnRider 7y ago

   Why is it bad?
3. 3.▲
   by xFlynnRider 7y ago

   I've come across that. And it looks awesome! Might give it a chance, why not. I see Tesseract's OCR engine is based on LSTM networks.
4. 4.▲
   by xFlynnRider 7y ago

   I like all of your suggestions. I've been thinking about using TinyYOLOv3 as well. Provided the training set is considerably bigger than my own (I've created about ~550 samples and fine-tuned the model with them), you could end up
5. 5.▲
   by xFlynnRider 7y ago

   Creator here. I get about 30FPS. The more compute power you throw in, the higher the framerate. It's really buttery smooth if I disable the recognition part and just leave in the detection. Since it's demo project (something I jus
6. 6.▲
   by xFlynnRider 7y ago

   Probably yeah, but the potential of the cloud was much more appealing to me. Detecting the license plates is really cheap computationally speaking, but not on the RPi. The most expensive part computationally was identifying the words (lette
7. 7.▲
   by xFlynnRider 7y ago

   I'm the creator here. Yeah, 20 K80s is a bit excessive. That's because cortex (cortexlabs), which is the ML-model-deployment platform didn't initially have multiprocessing on each of their replicas - so I was bound to using j