OmDet-Turbo Swin-Tiny
| Property | Value |
|---|---|
| Parameter Count | 115M |
| License | Apache 2.0 |
| Paper | Research Paper |
| Tensor Type | F32 |
| Downloads | 18,259 |
What is omdet-turbo-swin-tiny-hf?
OmDet-Turbo is a state-of-the-art zero-shot object detection model that combines transformer architecture with a Swin-Tiny backbone for efficient real-time performance. Developed by researchers at OmLab, it introduces an efficient fusion head for improved detection capabilities.
Implementation Details
The model leverages a transformer-based architecture optimized for real-time performance while maintaining high accuracy in open-vocabulary detection tasks. It supports both single image and batched inference, making it versatile for various deployment scenarios.
- Efficient fusion head architecture for improved detection
- Support for batched multi-image inference
- Customizable confidence thresholds and NMS parameters
- Integration with HuggingFace Transformers library
Core Capabilities
- Zero-shot object detection without prior training on specific classes
- Real-time processing capabilities
- Support for multiple objects and classes in a single pass
- Flexible text prompt integration for detection tasks
- Batch processing with different prompts per image
Frequently Asked Questions
Q: What makes this model unique?
OmDet-Turbo stands out for its efficient fusion head design and real-time processing capabilities while maintaining the flexibility of zero-shot detection. It can detect objects without prior training on specific classes, making it highly versatile for various applications.
Q: What are the recommended use cases?
The model is ideal for applications requiring real-time object detection with flexible class definitions, such as robotics, surveillance systems, automated inspection, and interactive AI systems. It's particularly useful when the objects to be detected aren't known beforehand or change frequently.
