T-Rex is an interactive object counting model that can first detect then count any objects through visual prompting

What is T-Rex 🦖

• T-Rex is an object counting model that can first detect then count any objects through visual prompting, which is highlighted by the following features:
• Open-Set: T-Rex possess the capacity to count any object, without constraints on predefined categories.
• Visual Promptable: Users can provide visual examples to specify the objects for counting.
• Intuitive Visual Feedback: T-Rex is a detection-based model that allows for visual feedback (i.e. detected boxes), enabling users to assess the accuracy of the result.
• Interactive: Users can actively participate in the counting process to rectify any errors.

How Does T-Rex Work ⚙️

• T-Rex provides three major workflows for interactive object counting / detection.
• Positive-only Prompt Mode: T-Rex can detect then count similar objects in an image with just a single click or box drawing. Additional visual prompts can be added for densely packed or small objects.
• Positive with Negative Prompt Mode: To address false detections caused by similar objects, users can correct the outcome by applying negative prompts to the erroneously detected objects.
• Cross Image Prompt Mode: This feature supports counting across different reference and target images, ideal for automatic annotation. Users prompt on one image, and T-Rex annotates the others automatically.

What Can T-Rex Do 📝

• T-Rex can be applyed to various domains for counting including but not limited to Agriculture, Industry, Livestock, Biology, Medical, Retail, Electronic, Transportation, Logistics, Human, etc.
• T-Rex can also serve as an open-set object detector, which can be applied for automatic annotaion. It process exponential zero-shot detection capability, and offers strong performance in dense and overlapping scenes.
• We list some of the potential applications of T-Rex below:

Try Demo 🚀

• Waiting for DDS

BibTeX 📚

Wating for technical report

Acknowledgement 🙏

• We would like to thank the DeepDataSpace team for building the demo.

TODO List 📝

• [ ] A more detailed version of paper
• [ ] Release the code