Filter Duplicate Images using CLIP

Efficient management and processing of large image datasets hinge critically on accurate duplicate image detection, a pivotal task for ensuring data integrity and optimizing computational resources. Conventional methods often falter under the strain of high memory consumption and computational complexity inherent in comparing images across expansive datasets. This study delves into advanced methodologies aimed at achieving precise and resource-efficient duplicate image identification. Leveraging the CLIP (Contrastive Language-Image Pretraining) model, renowned for its robust visual-semantic embeddings, we have achieved a noteworthy accuracy of 98% in detecting duplicate images. Our approach harnesses the power of multithreading and GPU acceleration to bolster processing speed and streamline resource utilization. Through extensive experimentation and evaluation on diverse image datasets, our findings underscore significant strides in enhancing duplicate image detection capabilities. This research contributes valuable insights into optimizing data management practices within largescale image processing applications, paving the way for more effective and efficient handling of image datasets.