Update README.md

Author: filipradenovic

README.md

@@ -215,17 +215,14 @@ Navigate (```cd```) to the root of the toolbox ```[YOUR_CIRTORCH_ROOT]```.
   ```
   for `ResNet50`, `ResNet101`, or `ResNet152`, respectively. 
 
-  <details>
-  <summary>Implementation details:</summary><br/>
+  Implementation details:
 
   - Whitening FC layer is initialized in a supervised manner using our training data and off-the-shelf features.
   - Whitening FC layer is precomputed for popular architectures and pooling methods, see [imageretrievalnet.py#L50](https://github.com/filipradenovic/cnnimageretrieval-pytorch/blob/474b1fe61ff0e8a6f076ef58f7334cf33d7a3773/cirtorch/networks/imageretrievalnet.py#L50) for the full list of precomputed FC layers.
   - When whitening is added in the fine-tuning procedure, we notice that the performance is highest if the images are with a similar high-resolution at train and test time. 
   - When whitening is added, the distribution of pairwise distances changes significantly, so roughly twice larger margin should be used for contrastive loss. In this scenario, triplet loss performs slightly better. 
   - Additional tunning of hyper-parameters can be performed to achieve higher performance or faster training. Note that, in this example, `--neg-num` and `--image-size` hyper-parameters are chosen such that the training can be performed on a single GPU with `16 GB` of memory. 
 
-  </details>
-    
 </details>
 
 <details>