Object detection is a fundamental task in computer vision, enabling AI models to identify and localize objects within an image. This notebook demonstrates how to use the Gemini API for object detection, including:
🔹 Single & Multi-Class Object Detection
🔹 Attribute-Based Recognition (e.g., Detecting red umbrellas, white dresses)
🔹 Negative Object Detection (Ensuring absent objects are not falsely identified)
🔹 World Knowledge for Object Identification (e.g., Recognizing Dog Breeds)
🔹 Reading Handwritten Text & Detecting Objects Referenced in Text
🔹 Spatial Reasoning & Scene Understanding
This work showcases how Large Vision-Language Models (VLMs) can analyze, reason, and interact with images using advanced AI techniques.
For additional interactive applications, check out this demo.
This notebook successfully demonstrates Object Detection with Gemini API using Open-Vocabulary Vision-Language Models (VLMs).
🔹 Object Detection in Various Scenarios ✔ Open-vocabulary object detection ✔ Multi-class detection with attribute filtering (e.g., red umbrellas, white dresses) ✔ Negative detection (ensuring absent objects are ignored)
🔹 Advanced AI Capabilities ✔ World Knowledge Integration (Identifying dog breeds) ✔ Handwritten Text-Based Object Detection ✔ Spatial Reasoning & Scene Understanding
🔹 Applications in Real-World Use Cases ✔ Automated object counting and classification ✔ AI-powered visual question answering (VQA) ✔ Security & surveillance analysis ✔ Retail and e-commerce product recognition
Future Enhancements:
- Combine Gemini’s Vision API with object tracking for real-time applications.
- Extend VQA (Visual Question Answering) capabilities for deeper scene understanding.
- Explore text-conditioned object retrieval (e.g., “Find the blue backpack in the image”).