Update README.md

README.md

@@ -33,20 +33,7 @@ _[Hamidreza Hashempoor](https://hamidreza-hashempoor.github.io/),  Yu Dong Hwang
 
 ## Dataset Overview
 
-GT format is like (each line):
-`frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, 1.0`.
 
-To prepare the dataset, first run `extract_frames.py` to decode frames from each video.  
-In **line 11** of the script, add the video filename and the number of frames you want to extract.  
-```bash
-python extract_frames.py
-```
-
-Then, convert the ground truth into COCO format with:
-```bash
-python convert_to_coco.py
-```
-This will generate annotations/train.json ready for training your detector.
 
 Brief statistics and visualization of FastTracker benchmark and its comparison with other benchmarks.
 
@@ -84,7 +71,22 @@ Brief statistics and visualization of FastTracker benchmark and its comparison w
 
 ### Data Format
 
-The FastTrack benchmark follows the [MOTChallenge](https://motchallenge.net/) standard annotation format. Each ground truth file (`gt/gt.txt`) contains a list of object annotations per frame in CSV format with the following 10 columns:
+GT format is like (each line):
+`frame, id, bb_left, bb_top, bb_width, bb_height, conf, class, 1.0`.
+
+To prepare the dataset, first run `extract_frames.py` to decode frames from each video.  
+In **line 11** of the script, add the video filename and the number of frames you want to extract.  
+```bash
+python extract_frames.py
+```
+
+Then, convert the ground truth into COCO format with:
+```bash
+python convert_to_coco.py
+```
+This will generate annotations/train.json ready for training your detector.
+
+
 
 ## Citation
 If you use our code or Benchmark, please cite our work.