feat: add readme for describe two label dataset construction

README.md

@@ -13,6 +13,16 @@ This DETR model is equipped with a robust post-processing pipeline, incorporatin
 ## Dataset
 Introducing a cutting-edge approach to accident detection, this model employs the DETR (DEtection Transfomer) architecture, specifically designed to seamlessly identify accidents within a comprehensive scene captured in a single image. Unlike traditional methods, this innovative model operates within the context of full images, leveraging the power of transformer-based object detection.
 
+Table 1: When we use dataset focuses on accident label, model fails to detect accidents when traffic jams.
+| traffic jams | traffic jams |
+|-------|-------|
+| ![traffic jams](./demo/traffic-jams-3.png) | ![traffic jams](./demo/traffic-jams-4.png) |
+
+Table 2: When we use multi label dataset (accident and vehicle), model can detect accidents accurately without reducing detection performance when traffic jams 
+| traffic jams | traffic jams | accident | accident |
+|-------|-------|------|-------|
+| ![traffic jams](./demo/traffic-jams-1.png) | ![traffic jams](./demo/traffic-jams-2.png) | ![accident](./demo/accident-1.png) | ![accident](./demo/accident-2.png) |
+
 Trained on a diverse and multilabel dataset, including 'accident' and 'vehicle' labels, the model excels in simultaneously recognizing both accident-related incidents and the presence of vehicles. This dual-label dataset enhances the model's capacity to comprehensively understand and interpret complex traffic scenarios, making it a potent tool for real-time accident detection and analysis.
 
 By adopting a holistic perspective on the entire image, this DETR-based model contributes to a more robust and nuanced understanding of potential accidents, fostering advancements in automated safety systems. Its proficiency in detecting accidents within the broader context of traffic scenes positions it as a valuable asset for applications dedicated to enhancing road safety and emergency response.