RateMe is a neural network that allows you to recognize gestures of thumb up and thumb down
Rating system using computer vision
RateMe is a neural network that allows you to recognize gestures of thumb up and thumb down. The algorithm can be embedded in your project and automate the process of evaluation of something or someone.
For example, using Rate Me you can:
Rate music on the YouTube Music service, when you are uncomfortable with clicking buttons
Count the votes in a beauty contest to determine the winner
Rate drinks and meals during their tasting
jupyter for jupyter-notebook
pip3 install opencv-contrib-python numpy
Test_RateMe.ipynb to test RateMe on example images
(for that you need to start
jupyter-notebook, it will open window in your browser, where you need to select
For example of realtime detection run
Minimal working example:
import cv2 from RateMe.utils import RateMe img = cv2.imread('test_imgs/like.jpg') label = net.decode(img)
RateMe is based on tiny-YOLOv3 architecture.
It's accuracy of thumb up/down gesture recognition is calculated as mean average precision (mAP@0.25) = 0.851941, or 85.19%; average IoU = 73.89%
The neural network has been trained on ~3K images (taken from different angles photos of people showing their thumbs or not). Images were labeled using the labelImg program.
Class labels: 0 -- "Like (thumb up)", 1 -- "Dislike (thumb down)"
Full pipeline speed is 6-7 FPS on Intel(R) Core(TM) i5-4300M CPU @ 2.60GHz.
~100ms on frame grabbing
~100ms on neural network inference
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.