highly-optimized Faster RCNN baseline on the challenging COCO object detection dataset. Moreover, DETR can be easily
generalized to produce panoptic segmentation in a unified manner. We show that it significantly outperforms competitive
baselines.
This model was contributed by nielsr. The original code can be found here.
The quickest way to get started with DETR is by checking the example notebooks (which showcase both inference and
fine-tuning on custom data).
Here's a TLDR explaining how [~transformers.DetrForObjectDetection] works:
First, an image is sent through a pre-trained convolutional backbone (in the paper, the authors use
ResNet-50/ResNet-101). Let's assume we also add a batch dimension. This means that the input to the backbone is a
tensor of shape (batch_size, 3, height, width), assuming the image has 3 color channels (RGB). The CNN backbone
