Update image

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}
 }
 ```