Compared with the previous YOLOv3, YOLOv4 has the following advantages: - It is an efficient and powerful object detection model that enables anyone with a 1080 Ti or 2080 Ti GPU to train a super fast and accurate object detector. - The influence of state-of-the-art «Bag-of-Freebies» and «Bag-of-Specials» object detection methods during detector training has been verified. - The modified state-of-the-art methods, including CBN (Cross-iteration batch normalization), PAN (Path aggregation network), etc., are now more efficient and suitable for single GPU training.
On Guard Against COVID-19: AI Projects That Deserve a Shout-Out
In this article, the author analyzed some of the most interesting and promising solutions in terms of their potential to slow down the spread of the COVID-19 infection, reduce death rates, and improve information hygiene in these turbulent times.
AWS Machine Learning Blog: Reduce ML inference costs on Amazon SageMaker for PyTorch models using Amazon Elastic Inference https://go.aws/2QtpruT
Today, we are excited to announce that you can now use Amazon Elastic Inference to accelerate inference and reduce inference costs for PyTorch models in both Amazon SageMaker and Amazon EC2. PyTorch is a popular deep learning framework that uses dynamic computational graphs. This allows you to easily develop deep learning models with imperative and […]