Update README.md

README.md

@@ -22,13 +22,12 @@ Tracks have a unique ID (among a file) so you may extract all the detections for
 track_data = df[df["unique_track_identifier"] == "2022_09_21_astor_place_landfill_0000_0"]
 ```
 
-For each detection columns `xmin, xmax, ymin, ymax` indicates the bounding box in pixel coordinates (note that the images are equirectangular with size 3840x1920 but bounding boxes may appear with xmin > 3840 which indicates a wrapping around : xmin = 4240 is equivalent to xmin = 400)
-For each detection columns `sapiens_308_[JOINTNAME]_[x,y,score]` contains the pixel coordinates and confidence for detections using Sapiens with Goliath 308 keypoints format
-For each detection columns `vitpose_[JOINTNAME]_[x,y,score]` contains the pixel coordinates and confidence for detections using VitPose with COCO 17 keypoints format
+- For each detection columns `xmin, xmax, ymin, ymax` indicates the bounding box in pixel coordinates (note that the images are equirectangular with size 3840x1920 but bounding boxes may appear with xmin > 3840 which indicates a wrapping around : xmin = 4240 is equivalent to xmin = 400)
+- For each detection columns `sapiens_308_[JOINTNAME]_[x,y,score]` contains the pixel coordinates and confidence for detections using Sapiens with Goliath 308 keypoints format
+- For each detection columns `vitpose_[JOINTNAME]_[x,y,score]` contains the pixel coordinates and confidence for detections using VitPose with COCO 17 keypoints format
+- The `mask_rle` column contains the RLE encoded binary mask of the person in the image. RLE encoding / decoding functions :
 
-The `mask_rle` column contains the RLE encoded binary mask of the person in the image. RLE encoding / decoding functions :
-
-```
+```python
 import torch
 
 def encode_RLE(mask):