Tag: computer vision
-
Organizations, Members & Access Level Management on the AIEX Platform
Training a highly accurate computer vision model requires many carefully annotated images. Data gathering and annotation take time and might often prove to be hard. As the saying goes, nothing is particularly hard if you divide it into small jobs (and assign it to a team of people). This chapter details the teamwork tools implemented…
-
Important Computer Vision Datasets
This article reviews famous datasets in the field of computer vision.
-
Dataset Development Lifecycle
Google has come up with a framework for data collection inspired by software development concepts in a 5-step cyclical process. In this article, we will examine google’s proposed data collection framework.
-
Attention Mechanism: from NLP to Computer Vision
In this article, we discuss the Attention mechanism and trace its history of use from natural language processing to computer vision. And finally, we will examine transformers and their application in computer vision.
-
Trauma Detection on Pelvic Radiographs using Computer Vision Algorithms
A timely and accurate diagnosis of the proximal femur and pelvis injuries in trauma patients is essential to saving their lives. High-quality clinical trauma care and treatment require both physician experience and radiography images. A multiscale deep learning algorithm called PelviXNet has been developed to rapidly and accurately detect most kinds of pelvic and hip…
-
Classify Broken and Normal Bones in X-ray Images using deep learning
Nowadays with the help of computer vision technology and image processing we can classify broken and normal bone X-ray images with high accuracy. In this article, we will discuss the Deep Learning approach for this purpose.
-
Annotating Computer Vision Projects
Choosing the right images for training, validating, and testing computer vision algorithms will significantly affect your AI project’s success. To train an AI model for object detection, segmentation, and classification with human-like performance, each image in the dataset must be labeled thoughtfully and accurately. This article examines dataset annotation and labeling techniques.
-
What Is Data Augmentation ?
To train a model or use transfer learning in machine vision, there must be enough data. Data Augmentation is a very important step that helps us increase our training data. The purpose of this article is to examine this feature and to review the techniques used to increase data on the AIEX platform.
-
Model-Driven Vs Data-Driven Approach
An AI model’s performance can be increased by either improving the dataset or the model’s structure. The purpose of this article is to examine these two approaches and determine which one is the most efficient.