Update README

README.md

@@ -51,12 +51,12 @@ Here is an overview of the important TrackLab classes:
   - Example: **[OfflineTrackingEngine](pbtrack/engine/offline.py)**: The offline tracking engine performs tracking one module after another to speed up inference by leveraging large batch sizes and maximum GPU utilization. For instance, YoloV8 is first applied on an entire video by batching multiple images, then the re-identification model is applied on all detections in the video, etc. 
 - **[Pipeline](pbtrack/pipeline/module.py)**: Define the order in which modules are executed by the TrackingEngine. If a tracker_state is loaded from disk, modules that should not be executed again must be removed.
   - Example: [bbox_detector, reid, track, jn_detect, jn_tracklet]
-- **[VideoLevelModule](pbtrack/pipeline/videolevel_module.py)**: Abstract class to be instantiated when adding a new tracking module that operates at the video level, i.e. when it operates on all frames simultaneously. Can be used to implement offline tracking strategies, tracklet level voting mechanisms, etc. 
+- **[VideoLevelModule](pbtrack/pipeline/videolevel_module.py)**: Abstract class to be instantiated when adding a new tracking module that operates on all frames simultaneously. Can be used to implement offline tracking strategies, tracklet level voting mechanisms, etc. 
   - Example: [VotingTrackletJerseyNumber](pbtrack/wrappers/jn_detector/voting_tracklet_jn_api.py): To perform majority voting within each tracklet and compute a consistent tracklet level jersey number. Update the "jersey_number" column within `detections_pred`.
-- **[ImageLevelModule](pbtrack/pipeline/imagelevel_module.py)**: Abstract class to be instantiated when adding a new tracking module that operates at the image level, i.e. when it operates on a single frame. Can be used to implement online tracking strategies, pose/segmentation/bbox detectors, etc.
+- **[ImageLevelModule](pbtrack/pipeline/imagelevel_module.py)**: Abstract class to be instantiated when adding a new tracking module that operates operates on a single frame. Can be used to implement online tracking strategies, pose/segmentation/bbox detectors, etc.
   - Example 1: [YOLOv8](pbtrack/wrappers/detect_multiple/yolov8_api.py): To perform object detection on each image with [YOLOv8](https://github.com/ultralytics/ultralytics). Create a new row (i.e. detection) within `detections_pred`.
   - Example 2: [StrongSORT](pbtrack/wrappers/track/strong_sort_api.py): To perform online tracking with [StrongSORT](https://github.com/dyhBUPT/StrongSORT). Creates a new "track_id" column for each detection within `detections_pred`. 
-- **[DetectionLevelModule](pbtrack/pipeline/detectionlevel_module.py)**: Abstract class to be instantiated when adding a new tracking module that operates at the detection level, i.e. when it operates on a single detection. Can be used to implement pose estimation for top-down strategies, re-identification, attributes recognition, etc. 
+- **[DetectionLevelModule](pbtrack/pipeline/detectionlevel_module.py)**: Abstract class to be instantiated when adding a new tracking module that operates on a single detection. Can be used to implement pose estimation for top-down strategies, re-identification, attributes recognition, etc. 
   - Example 1: [EasyOCR](pbtrack/wrappers/jn_detector/easyocr_api.py): To perform jersey number recognition on each detection with [EasyOCR](https://github.com/JaidedAI/EasyOCR). Create a new "jersey_number" column within `detections_pred`.
   - Example 2: [BPBReId](pbtrack/wrappers/reid/bpbreid_api.py): To perform person re-identification on each detection with [BPBReID](https://github.com/VlSomers/bpbreid). Create a new "embedding" column within `detections_pred`.
 - **[Callback](pbtrack/callbacks/callback.py)**: Implement this class to add a callback that is triggered at a specific point during the tracking process, e.g. when task/dataset/video processing starts/ends.