The Squeeze-and-Excitation (SE) block is a novel architectural unit designed to enhance the representational power of convolutional neural networks (CNNs) by adaptively recalibrating channel-wise feature responses. The SE block explicitly models interdependencies between channels, allowing the network to selectively emphasize informative features and suppress less useful ones. This mechanism is implemented through a two-step process: the "squeeze" operation, which aggregates spatial information across channels to produce a channel descriptor, and the "excitation" operation, which uses a simple self-gating mechanism to generate per-channel modulation weights. These weights are then applied to the feature maps to produce the output of the SE block. SE blocks can be stacked to form SENet architectures that generalize effectively across different datasets. They have been shown to significantly improve the performance of existing state-of-the-art CNNs with only a slight increase in computational cost. The SE block was used in the ILSVRC 2017 classification submission, where it achieved a top-5 error of 2.251%, surpassing the previous year's winner by approximately 25%. The SE block is computationally lightweight and can be integrated into existing architectures with minimal modifications. It has been successfully applied to various architectures, including ResNet, Inception, and MobileNet, demonstrating its versatility and effectiveness. Experiments on multiple datasets, including ImageNet, Places365, and COCO, show that SE blocks consistently improve performance across different tasks and architectures. Ablation studies further confirm the importance of the squeeze and excitation operations, as well as the effectiveness of different integration strategies. The SE block's ability to dynamically recalibrate channel-wise features makes it a valuable addition to CNNs, enhancing their ability to capture important features and improve overall performance.The Squeeze-and-Excitation (SE) block is a novel architectural unit designed to enhance the representational power of convolutional neural networks (CNNs) by adaptively recalibrating channel-wise feature responses. The SE block explicitly models interdependencies between channels, allowing the network to selectively emphasize informative features and suppress less useful ones. This mechanism is implemented through a two-step process: the "squeeze" operation, which aggregates spatial information across channels to produce a channel descriptor, and the "excitation" operation, which uses a simple self-gating mechanism to generate per-channel modulation weights. These weights are then applied to the feature maps to produce the output of the SE block. SE blocks can be stacked to form SENet architectures that generalize effectively across different datasets. They have been shown to significantly improve the performance of existing state-of-the-art CNNs with only a slight increase in computational cost. The SE block was used in the ILSVRC 2017 classification submission, where it achieved a top-5 error of 2.251%, surpassing the previous year's winner by approximately 25%. The SE block is computationally lightweight and can be integrated into existing architectures with minimal modifications. It has been successfully applied to various architectures, including ResNet, Inception, and MobileNet, demonstrating its versatility and effectiveness. Experiments on multiple datasets, including ImageNet, Places365, and COCO, show that SE blocks consistently improve performance across different tasks and architectures. Ablation studies further confirm the importance of the squeeze and excitation operations, as well as the effectiveness of different integration strategies. The SE block's ability to dynamically recalibrate channel-wise features makes it a valuable addition to CNNs, enhancing their ability to capture important features and improve overall performance.