A story of one image recognition project. From optimizing an object detection model and optimizing a dataset to multistage neural networks.

A dive into developing an image recognition app without using neural networks

Explore the cutting-edge integration of YOLOv3 and PolyRNN++ for automated image analysis.

In this article, I’ll outline how we used Amazon Rekognition to rapidly build an image object detection feature for a social media startup.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

TM-CNN combines template matching and CNN to efficiently detect defects in magnetic labyrinthine patterns, reducing manual annotations and improving accuracy.

How to build a system that automatically detects and categorizes objects in media as soon as users upload it, storing the results in a database for future use.