Update README.md

README.md

@@ -25,7 +25,7 @@ pipeline_tag: object-detection
 
 ## Model Details
 
-![image/png](https://github.com/conditionedstimulus/hf_media/blob/main/dab_detr_convergence_plot.png)
+![image/png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dab_detr_convergence_plot.png)
 
 > We present in this paper a novel query formulation using dynamic anchor boxes for DETR (DEtection TRansformer) and offer a deeper understanding of the role of queries in DETR. This new formulation directly uses box coordinates as queries in Transformer decoders and dynamically updates them layer-by-layer. Using box coordinates not only helps using explicit positional priors to improve the query-to-feature similarity and eliminate the slow training convergence issue in DETR, but also allows us to modulate the positional attention map using the box width and height information. Such a design makes it clear that queries in DETR can be implemented as performing soft ROI pooling layer-by-layer in a cascade manner. As a result, it leads to the best performance on MS-COCO benchmark among the DETR-like detection models under the same setting, e.g., AP 45.7\% using ResNet50-DC5 as backbone trained in 50 epochs. We also conducted extensive experiments to confirm our analysis and verify the effectiveness of our methods.