Advancing state-of-the-art image recognition with deep learning on hashtags

What this post added

This post details a novel approach to advancing image recognition by training models on large-scale public image datasets labeled with user-supplied hashtags. It addresses the limitations of manual labeling for massive datasets by using hashtags as weak supervision. The post highlights the development of new approaches to handle multiple labels per image, hashtag synonyms, and balancing frequent/rare hashtags. It also describes the engineering effort involved in distributed training across 336 GPUs to shorten training time from over a year to a few weeks, and the development of duplicate removal methods. The research achieved record-high accuracy on ImageNet and demonstrated significant performance boosts on COCO object-detection, showcasing the effectiveness of weakly supervised pretraining with hashtags.