ConvNeXt Model

ConvNeXt is a convolutional neural network architecture designed to improve upon traditional CNNs by incorporating elements inspired by Vision Transformers, aiming for enhanced performance and efficiency in various computer vision tasks. Current research focuses on adapting ConvNeXt for diverse applications, including image classification, object detection, segmentation (particularly in medical imaging), and even audio processing, often incorporating techniques like model compression and self-supervised learning to optimize performance and resource usage. This work is significant due to ConvNeXt's demonstrated competitive performance across multiple domains, offering a powerful and adaptable framework for both scientific advancements and real-world applications requiring efficient and accurate image and signal processing.