Deep Learning for Image Recognition Stare of the Art Techniques And Future Trends (Journal of Advances and Scholarly Researches in Allied Education, 2024)
Deep learning approaches have completely transformed computer vision research, particularly in the areas of object identification and picture recognition. For tasks like as object identification and image recognition, we outline in this abstract the most recent developments and state-of-the-art methods in deep learning. The term "image recognition" describes the method by which objects or patterns inside digital images may be automatically identified and classified. One example is the exceptional performance shown by convolutional neural networks (CNNs) in picture identification tests. These algorithms can identify complicated patterns and provide accurate predictions because they learn hierarchical representations of visual attributes straight from raw pixel input. Applications such as item identification and image recognition have been revolutionised by models' capacity to learn complex visual representations directly from pixel input. Massive annotated datasets and advancements in deep learning architectures have sped up the process of creating very precise and efficient systems. Autonomous vehicles, surveillance, and medical imaging are just a few of the many areas that stand to benefit from deep learning's continued advancements in object identification and image recognition.