Algorithm and method for dynamically varying quantization precision of deep learning network

An image recognition method comprising:

• generating a plurality of quantization models corresponding to a plurality of different bit numbers by performing quantization corresponding to the plurality of bit numbers on a deep learning network which performs object recognition on any image;
• receiving image data as an input to the deep learning network;
• determining uncertainty of the received image data;
• selecting any one of the plurality of quantization models based on the determined uncertainty;
• and
• performing object recognition on the image data through the selected quantization model and outputting a label corresponding to the image data as an object recognition result,
• wherein the determining of the uncertainty comprises:
• calculating probabilities that the input image data will correspond to a plurality of classes related to object recognition of the deep learning network;
• and
• calculating an uncertainty score based on the probabilities calculated according to the plurality of classes,
• wherein the determining of the uncertainty is performed by an uncertainty determination network which is separate from the deep learning network, and
• the uncertainty determination network includes a smaller number of layers than the deep learning network.