Update README.md

README.md

@@ -146,12 +146,12 @@ Images are resized/rescaled such that the shortest side is at least 480 and at m
 
 ## Evaluation
 
-![image/png](https://github.com/conditionedstimulus/hf_media/blob/main/https://github.com/conditionedstimulus/hf_media/blob/main/results_dab_detr.png)
+![image/png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dab_detr_results.png)
 
 
 ### Model Architecture and Objective
 
-![image/png](https://github.com/conditionedstimulus/hf_media/blob/main/model_arch_dab_detr.png)
+![image/png](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/dab_detr_model_arch.png)
 
 Overview of DAB-DETR. We extract image spatial features using a CNN backbone followed with Transformer encoders to refine the CNN features.
 Then dual queries, including positional queries (anchor boxes) and content queries (decoder embeddings), are fed into the decoder to probe the objects which correspond to the anchors and have similar patterns with the content queries. The dual queries are updated layer-by-layer to get close to the target ground-truth objects gradually.