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The progress in technology has led to an increase in the amount of data that can be generated and processed. Downsampling methods are employed to eliminate unnecessary data but can also lead to the loss of valuable information. Herein, we designed a pooling algorithm, Conditional-Pooling , that provides a transitive structure composed of average (avg.) and max-pooling methods, and it hosts the advantageous behaviors from both. We examined our approach on several image recognition and detection tasks using deep neural networks. The results reveal that our method excels over major rivals like max-pooling and avg. pooling when tested on MNIST, Fashion-MNIST, and CIFAR for classification, Pascal VOC for detection, COCO minitrains, and ADORESet for both classification and detection. The output of Conditional-Pooling preserves important image features such as edges and corners and contains more salient features that lead to more accurate results. The code for Conditional-Pooling will be available at https://github.com/bayraktare/conditional_pooling. • Adaptive pooling for novel precision and accuracy especially for complex scenes. • Exceeding existing methods on popular visual tasks regarding various metrics. • Game-changing results for noise elimination and image downsampling. [ABSTRACT FROM AUTHOR]