From e4ac5ff8ddb26f2f891f0cd397134d1865fb95b6 Mon Sep 17 00:00:00 2001 From: Niels Rogge Date: Wed, 14 Sep 2022 07:33:16 +0000 Subject: [PATCH] Update image --- README.md | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index c69eb67..1a42d79 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ The DETR model is an encoder-decoder transformer with a convolutional backbone. 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 "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. -![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/detr_architecture.png) +![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png) ## Intended uses & limitations @@ -82,19 +82,12 @@ The Deformable DETR model was trained on [COCO 2017 object detection](https://co ```bibtex @misc{https://doi.org/10.48550/arxiv.2010.04159, doi = {10.48550/ARXIV.2010.04159}, - - url = {https://arxiv.org/abs/2010.04159}, - + url = {https://arxiv.org/abs/2010.04159}, author = {Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng}, - keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, - title = {Deformable DETR: Deformable Transformers for End-to-End Object Detection}, - publisher = {arXiv}, - year = {2020}, - copyright = {arXiv.org perpetual, non-exclusive license} } ``` \ No newline at end of file