The backbone of every computer vision application is the data used. The quality of data determines how good the final application will perform. It is needless to say that sometimes we need to collect ...
Since its inception, the YOLO family of object detection models has come a long way. YOLOv7 is the most recent addition to this famous anchor-based single-shot family of object detectors. It comes ...
In this blog post, we will be training YOLOv4 object detection model on a pothole detection dataset using the Darknet framework. Before we move further, let’s have an overview of the models that ...
In this blog post, we are fine tuning YOLOv5 models for custom object detection training and inference. Introduction The field of deep learning started taking off in 2012. Around that time, ...
Our last post on the MRNet challenge presented a simple way to approach it. There you learned to make a separate model for each disease. And ended up with three models. Time to up your game! Now ...
When approaching a problem using Machine Learning or Deep Learning, researchers often face a necessity of model tuning because the chosen method usually depends on various hyperparameters and used ...
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In 2007, right after finishing my Ph.D., I co-founded TAAZ Inc. with my advisor Dr. David Kriegman and Kevin Barnes. The scalability, and robustness of our computer vision and machine learning algorithms have been put to rigorous test by more than 100M users who have tried our products.
