LW DETR (Light Weight Detection Transformer) LW DETR, a Light Weight DEtection TRansformer model, is designed to be a real time object detection alternative that outperforms conventional convolutional (YOLO style) and earlier transformer based (DETR) methods in terms of speed and accuracy trade off. It was introduced in the paper LW DETR: A Transformer Replacement to YOLO for Real Time Detection by Chen et al. and first released in this repository. Disclaimer: This model was originally contributed by stevenbucaille in 🤗 transformers. Model description LW DETR is an end to end object detection model that uses a Vision Transformer (ViT) backbone as its encoder, a simple convolutional projector, and a shallow DETR decoder. The core philosophy is to leverage the power of transformers while implementing several efficiency focused techniques to achieve real time performance. Key Architectural Details: ViT Encoder: Uses a plain ViT architecture. To reduce the quadratic complexity of global self attention, it adopts interleaved window and global attentions. Window Major Organization: It employs a highly efficient window major feature map organization scheme for attention computation, whic…
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