Update README.md

README.md

@@ -25,7 +25,7 @@ In a nutshell, ATST-SED proposes a fine-tuning strategy for the pretrained model
 - **About batch sizes**: If you change the batch sizes when fine-tuning ATST-Frame (Stage 1/2), you might probably need to change the `n_epochs` and `n_epochs_warmup` in the configuration file `train/local/confs/stage2.yaml` correspondingly. The fine-tuning of ATST-SED is related to the batch sizes, you might not reproduce the reported results when using a smaller batch sizes. The ablation study of the batch size setups is shown in the model performance below.
 
 # Comparing with DCASE code
-For better understanding of SED community, our codes are developed based on the [baseline codes](https://github.com/DCASE-REPO/DESED_task/tree/master/recipes/dcase2023_task4_baseline) of [DCASE2023 challenge task 4](https://dcase.community/). Therefore, the training progress is build under [`pytorch-lightning`](https://lightning.ai/).
+To allow the SED community better understands the codes and implementation details, we developed the algorithm based on the [baseline codes](https://github.com/DCASE-REPO/DESED_task/tree/master/recipes/dcase2023_task4_baseline) of [DCASE2023 challenge task 4](https://dcase.community/). Namely, the training progress is build under [`pytorch-lightning`](https://lightning.ai/).
 
 we changed 
 - [dataset.dataio.dataset.py](https://github.com/Audio-WestlakeU/ATST-SED/blob/main/desed_task/dataio/datasets_atst_sed.py) with our implementation.