DETR (End to End Object Detection) model with ResNet 101 backbone DEtection TRansformer (DETR) model trained end to end on COCO 2017 object detection (118k annotated images). It was introduced in the paper End to End Object Detection with Transformers by Carion et al. and first released in this repository. Disclaimer: The team releasing DETR did not write a model card for this model so this model card has been written by the Hugging Face team. Model description The DETR model is an encoder decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi layer perceptron) for the bounding boxes. The model uses so called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "…
We use cookies for essential functionality and analytics. You can accept or reject analytics cookies.Cookie policy