Update model card to clarify fine-tuning objective: mitigating hallucination on out-of-distribution data

README.md

@@ -8,7 +8,7 @@ tags:
 - pytorch
 - kitti
 - autonomous-driving
-- fine-tuned
+- from-scratch
 pipeline_tag: object-detection
 datasets:
 - kitti
@@ -30,15 +30,15 @@ model-index:
       value: "TBD"
 ---
 
-# YOLOv10 - KITTI Object Detection Fine-tuned
+# YOLOv10 - KITTI Object Detection Vanilla
 
-YOLOv10 model fine-tuned on KITTI dataset for enhanced autonomous driving object detection.
+YOLOv10 model fine-tuned on KITTI dataset to mitigate hallucination on out-of-distribution data for enhanced autonomous driving object detection.
 
 ## Model Details
 
 - **Model Type**: YOLOv10 Object Detection
 - **Dataset**: KITTI Object Detection
-- **Training Method**: fine-tuned
+- **Training Method**: fine-tuned to mitigate hallucination on out-of-distribution data
 - **Framework**: PyTorch/Ultralytics
 - **Task**: Object Detection
 
@@ -78,7 +78,9 @@ for result in results:
 
 ## Model Performance
 
-This model was fine-tuned on the KITTI Object Detection dataset using YOLOv10 architecture.
+This model was fine-tuned to mitigate hallucination on out-of-distribution data on the KITTI Object Detection dataset using YOLOv10 architecture.
+
+**Fine-tuning Objective**: This model was specifically fine-tuned to mitigate hallucination on out-of-distribution (OOD) data, improving robustness when encountering images that differ from the training distribution.
 
 ## Intended Use