PyTorch Conference Europe 2026

Nowadays, Vision-Language Models (VLMs) have plenty of different applications. However, it must be pointed out that we cannot be totally sure that they are the most accurate and precise solution for all potential problems. We must compare their possibilities with some other pipelines. In this presentation, we would like to compare on-premise models – Qwen 3 and InternVL-3.5, and cloud-based solutions – Gemini 3, GPT-5 with traditional pipeline based on YOLOv11 and image processing techniques. The battlefield will be ball detection and tracking in soccer matches recordings (from different angles and in diversified light, e.g., sunny, night, and weather conditions, e.g., snowy, rainy day) downloaded from SoccerNet database. In this case, we used both broadcast videos and action and replay images. All of them were marked manually to prepare ground truth database. The models must recognize not only the ball but also track it through the whole sequence of images. To give equal chances we fine-tuned YOLOv11 and provided additional knowledge to VLMs in the form of RAG pipeline. Comparison was made with traditional Machine Learning metrics like accuracy, precision, and recall.