Conditional DETR model with ResNet 50 backbone Conditional DEtection TRansformer (DETR) model trained end to end on COCO 2017 object detection (118k annotated images). It was introduced in the paper Conditional DETR for Fast Training Convergence by Meng et al. and first released in this repository. Model description The recently developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross attention mechanism for fast DETR training. Our approach is motivated by that the cross attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi head cross attention. The benefit is that through the conditional spatial query, each cross attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region insid…
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