There are several online demos that showcase AI-powered object detection in action. One of the best examples is the TensorFlow Object Detection API demo. This open-source demo allows users to upload images and run pre-trained models for detecting various objects such as people, cars, and animals. The interface is simple, allowing users to experiment with different models and fine-tune parameters for better performance. Another excellent demo is Darknet's YOLO (You Only Look Once) demo, which provides real-time object detection for video streams and images. YOLO is known for its speed and accuracy in detecting objects and is widely used in research and industry applications. Additionally, Microsoft’s Custom Vision platform offers a user-friendly interface where you can upload your images, train custom models, and detect objects. This demo is particularly useful for those who need tailored solutions, as it allows for the training of object detection models based on specific datasets. Roboflow also offers an interactive object detection demo, where users can quickly train models on their own dataset and deploy them. These demos are ideal for developers looking to experiment with object detection models and refine their skills.
What are the best AI object detection demos online?

- The Definitive Guide to Building RAG Apps with LangChain
- Natural Language Processing (NLP) Advanced Guide
- GenAI Ecosystem
- Large Language Models (LLMs) 101
- How to Pick the Right Vector Database for Your Use Case
- All learn series →
Recommended AI Learn Series
VectorDB for GenAI Apps
Zilliz Cloud is a managed vector database perfect for building GenAI applications.
Try Zilliz Cloud for FreeKeep Reading
How can AR applications be optimized for battery life?
To optimize AR applications for battery life, developers should focus on reducing the overall power consumption of the a
What are the risks of using NLP in sensitive areas like law enforcement?
Using NLP in sensitive areas like law enforcement poses significant risks, including bias, ethical concerns, and account
What is the future potential of self-supervised learning?
Self-supervised learning (SSL) holds significant future potential, particularly in how it can transform various fields w