Grounding DINO Tiny
| Property | Value |
|---|---|
| Parameter Count | 172M |
| License | Apache 2.0 |
| Paper | View Paper |
| Framework | PyTorch |
What is grounding-dino-tiny?
Grounding DINO Tiny is a lightweight variant of the Grounding DINO architecture designed for zero-shot object detection. It combines DINO's detection capabilities with grounded pre-training, enabling open-set object detection through natural language queries. Despite its compact size of 172M parameters, it achieves impressive performance on standard benchmarks.
Implementation Details
The model implements a hybrid architecture that combines a vision transformer backbone with a text encoder. It processes both image and text inputs simultaneously, allowing for flexible object detection based on textual descriptions. The model works with PyTorch and uses Safetensors for efficient weight storage.
- Zero-shot capability eliminates the need for task-specific training
- Supports dynamic text queries for object detection
- Optimized for efficiency with tiny architecture variant
Core Capabilities
- Open-set object detection without additional training
- Text-guided object localization
- Support for multiple object classes in a single query
- Efficient inference with reduced parameter count
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ability to perform zero-shot object detection using natural language queries, while maintaining a relatively small parameter count of 172M. It bridges the gap between vision and language understanding in a lightweight package.
Q: What are the recommended use cases?
The model is ideal for applications requiring flexible object detection without pre-defined categories. Common use cases include content moderation, image analysis, and general-purpose object detection where new object categories may need to be detected without retraining.
