DeformableDetr is a novel object detection algorithm that enhances the performance of traditional DETR by introducing deformable convolution. It overcomes the poor performance of traditional DETR when dealing with target deformation, allowing the model to better adapt to targets of different shapes and scales.
1. Algorithm interpretation.
Deformable convolution: Traditional convolution operations use a fixed convolution kernel when extracting features, ignoring changes in the shape of the target. DeformableDetr allows the convolution kernel to adaptively adjust to the shape of the target when extracting features by introducing deformable convolutions.
Target deformation modeling: During training, deformabledetr uses deformation modeling losses to optimize the parameters of deformable convolution, so that the model can better adapt to the target deformation.
End-to-end training: DeformableDetr uses an end-to-end training method that combines feature extraction, object detection, and deformable convolution optimization to simplify the training process and improve model performance.
2. Examples of technology.
Due to space limitations, here is a simplified deformabledetr implementation framework for reference:
python reproduction**.
** above,deformconv2dThe class defines a deformable convolutional layer, which accepts an input tensorinputand Offset Tensorsoffsetas input, and the convolution result is returned. In the training process, by optimizing the parameters of the offset tensor, the convolution kernel can adaptively adjust the shape to better adapt to the target deformation.