Update README.md

README.md

@@ -8,7 +8,7 @@ base_model:
 - jameslahm/yolov10l
 pipeline_tag: object-detection
 ---
-# Layer Freezing and Transformer-Based Data Curation for Enhanced Transfer Learning in YOLO ArchitecturesCuration for Enhanced Transfer Learning in YOLO
+# Layer Freezing and Transformer-Based Data Curation for Enhanced Transfer Learning in YOLO Architectures
 
 ## Abstract
 The You Only Look Once (YOLO) architecture has transformed real-time object detection by performing detection, localization, and classification in a single pass. Despite its efficiency, balancing accuracy with computational resources remains a challenge, particularly in resource-constrained environments. This research investigates the impact of layer freezing in YOLO models, a transfer learning technique that enhances model adaptability without extensive retraining. We explore various YOLO configurations, including YOLOv8 and YOLOv10, across four datasets selected for their relevance to real-world applications, particularly in monitoring and inspecting critical infrastructure, including scenarios involving unmanned aerial vehicles (UAVs). Our findings show that freezing selected layers can significantly reduce training time and GPU consumption while maintaining or even surpassing accuracy compared to traditional fine-tuning. In particular, the small YOLOv10 variant with layer freezing achieved a mAP@50 of 0.84 on one of the datasets, representing a 28% reduction in GPU usage and a nearly 3% increase in mAP compared to full fine-tuning. Additionally, while we did not focus solely on improving the mean Average Precision (mAP) metrics, we aimed to maintain performance with less data, effectively capturing the source distribution more efficiently. For three of the four datasets we have worked with, we achieved a 3% reduction in both mAP@50 and mAP@50:95 scores while using 30% less training data by curating the training portion of the datasets using a strategy involving Vision Transformers and a cosine similarity metric.