Indian Traffic VQA Dataset

🧭 Overview Indian Traffic VQA is a real-world Visual Question Answering (VQA) dataset focusing on Indian road traffic signboards.The dataset is designed for training and evaluating Vision-Language Models (VLMs) and VQA systems in the traffic and transportation domain. This dataset bridges a gap between real-world Indian traffic conditions and machine understanding — ideal for research in autonomous driving, smart city AI, and traffic sign recognition under natural environments. 📦 Dataset Summary Images: 1,085 real-world traffic signboard images Questions: 4,341 unique questions Answers: Short, ground-truth textual responses Source: All images were collected using a mobile phone in real Indian road environments Format: .csv file with the following columns: image_name — name of the image file question — text-based query answer — corresponding ground-truth answer. The traffic512final.zip file contains all the 1085 images with 512x512 resolution. There are two .csv files attached. traffic_vqa_1085.csv contains one question and one answer, traffic_vqa_4341.csv contains multiple questions and answers per image. The first .csv file can be used for low resource computational environment. 🧠 Task Definition Given an image of a traffic signboard and a related question, the model must predict a short text answer. Example: image_name question answer image_0001.jpg What does this sign indicate? Speed Limit image_0002.jpg What does this sign show? Stop image_0003.jpg Is U-turn allowed here? No 🧩 Applications Visual Question Answering (VQA) Vision-Language Model (VLM) Fine-tuning Multimodal classification of traffic signs Dataset for benchmarking model reasoning in domain-specific visual data 🧰 Data Collection Details Captured in diverse Indian traffic conditions (urban, rural, highways) Includes varying lighting, occlusions, and view angles All images are real photographs, not synthetic