Update README.md

README.md

@@ -20,27 +20,26 @@ language:
 
 Official model weights for the paper **"From Detection to Association: Learning Discriminative Object Embeddings for Multi-Object Tracking"** (CVPR 2026).
 
+![image](https://cdn-uploads.huggingface.co/production/uploads/663063e9ce65a66ed8d90aff/jY6efxZCU3eWPWXi8Mp65.png)
+
 > **TL;DR.** We reveal that DETR-based end-to-end MOT suffers from overly similar object embeddings. FDTA explicitly enhances discriminativeness in this paradigm.
 
 
 ## Available Checkpoints
 
-| File | Dataset | Training Split |
-|------|---------|----------------|
+| File | Dataset | 
+|------|---------|
 | `dancetrack.pth` | DanceTrack |
 | `sportsmot.pth` | SportsMOT | 
 
 ## Main Results
-
 ### DanceTrack
-
 | Training Data | HOTA | IDF1 | AssA | MOTA | DetA |
 |---------------|------|------|------|------|------|
 | train         | 71.7 | 77.2 | 63.5 | 91.3 | 81.0 |
 | train+val     | 74.4 | 80.0 | 67.0 | 92.2 | 82.7 |
 
 ### SportsMOT
-
 | Training Data | HOTA | IDF1 | AssA | MOTA | DetA |
 |---------------|------|------|------|------|------|
 | train         | 74.2 | 78.5 | 65.5 | 93.0 | 84.1 |
@@ -52,7 +51,6 @@ Official model weights for the paper **"From Detection to Association: Learning
 | train         | 72.2 | 84.2 | 74.5 | 78.2 | 70.1 |
 
 ## Usage
-
 ### 1. Download Checkpoints
 
 ```python
@@ -65,11 +63,8 @@ ckpt_path = hf_hub_download(
     local_dir="./checkpoints/"
 )
 ```
-
 For full training and evaluation instructions, please refer to the [GitHub repository](https://github.com/Spongebobbbbbbbb/FDTA).
 
-
-
 ## Citation
 
 ```bibtex