Deformable ConvNets introduce two modules—deformable convolution and deformable RoI pooling—to enhance CNNs' ability to model geometric transformations. Deformable convolution adds 2D offsets to the regular grid sampling locations in standard convolution, enabling adaptive sampling based on input features. Deformable RoI pooling adds offsets to bin positions in RoI pooling, allowing adaptive part localization for objects with varying shapes. Both modules are lightweight, adding minimal parameters and computation, and can be easily integrated into existing CNNs for end-to-end training. These modules improve performance in tasks like object detection and semantic segmentation by learning dense spatial transformations from data. Experiments show that deformable ConvNets outperform traditional CNNs in these tasks, with significant improvements in accuracy and efficiency. The approach is effective for complex vision tasks, offering a flexible and efficient solution for modeling geometric transformations in deep learning.Deformable ConvNets introduce two modules—deformable convolution and deformable RoI pooling—to enhance CNNs' ability to model geometric transformations. Deformable convolution adds 2D offsets to the regular grid sampling locations in standard convolution, enabling adaptive sampling based on input features. Deformable RoI pooling adds offsets to bin positions in RoI pooling, allowing adaptive part localization for objects with varying shapes. Both modules are lightweight, adding minimal parameters and computation, and can be easily integrated into existing CNNs for end-to-end training. These modules improve performance in tasks like object detection and semantic segmentation by learning dense spatial transformations from data. Experiments show that deformable ConvNets outperform traditional CNNs in these tasks, with significant improvements in accuracy and efficiency. The approach is effective for complex vision tasks, offering a flexible and efficient solution for modeling geometric transformations in deep learning.