Multi-label-Image-Classification "I developed and deployed a deep learning model for multilabel image classification, distinguishing it from traditional binary and multiclass approaches.

Convolutional neural network (CNN) has shown great success in single-label image classification, but in real world images generally have multiple labels. In this paper, we utilize long short-term ...

Image classification is one of the trending applications in machine learning. It has a wide range of applications — from facial recognition algorithms to identifying complex patterns in images like ...

Multi-label image data is becoming ubiquitous. Image semantic understanding is typically formulated as a classification problem. This paper focuses on multi-label active learning for image ...

Image classification is one of AI's most common tasks, where a system is required to recognize an object from a given image. Yet real life requires us to recognize not a single standalone object but ...

For multi-label image classification, VOC 2007 dataset is divided into training (2,501), validation (2,510) and testing (4,952) sets. Following the previous work, we usually use the train and validate ...