diff --git a/README.md b/README.md
index 0033fd0..7a57142 100644
--- a/README.md
+++ b/README.md
@@ -66,7 +66,6 @@ python test.py --clip_path ./sample_videos/clips/v32 \
 
 Note that we use 216\*384 images for training, which has aspect ratio of 1:2. During inference, we scale the input to this size and then rescale the output back to the original size.
 
-<<<<<<< HEAD
 ## Train
 
 We also provide training code for reference. The training can be started by running:
@@ -80,20 +79,13 @@ python --data_root [root of video samples] \
 We do not provide the full video dataset due to the copyright issue. For image samples, we retrieve semantically similar images from ImageNet using [this repository](https://github.com/hmmlillian/Gray-Image-Retrieval). Still, one can refer to our code to understand the detailed procedure of augmenting the image dataset to mimic the video frames.
 
 ## Comparison with State-of-the-Arts
-=======
-## Comparison with State-of-the-arts
->>>>>>> c90714cca0040f28a140d9f0043f05747056b82b
 
 <figure>
 <img src='assets/sample1.png'/>
 </figure>
 
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 ## More results
 
-=======
-## More Results
->>>>>>> c90714cca0040f28a140d9f0043f05747056b82b
 <figure>
 <img src='assets/sample2.png' />
 </figure>
@@ -104,11 +96,6 @@ We do not provide the full video dataset due to the copyright issue. For image s
 
 :star: Please check our [Youtube demo](https://youtu.be/HXWR5h5vVYI) for results of video colorization.
 
-<!-- ## TODO
-
-- [ ] Release the training code
-- [ ] Prepare the Colab demo -->
-
 ## Citation
 
 If you use this code for your research, please cite our paper.