Grounding DINO Base Model
| Property | Value |
|---|---|
| Parameter Count | 233M |
| License | Apache 2.0 |
| Paper | View Paper |
| Downloads | 1.18M+ |
What is grounding-dino-base?
Grounding DINO base is an advanced vision model that bridges the gap between text and visual object detection. Developed by IDEA-Research, it extends traditional closed-set object detection by incorporating a text encoder, enabling zero-shot detection capabilities without pre-trained labels.
Implementation Details
The model employs a sophisticated architecture that combines DINO (Detection Transformer) with grounded pre-training. It processes both image and text inputs simultaneously, allowing for flexible object detection based on textual descriptions. The model achieves impressive results, including 52.5 AP on COCO zero-shot detection tasks.
- Transformer-based architecture with 233M parameters
- Supports both PyTorch and Safetensors implementations
- Implements zero-shot object detection pipeline
Core Capabilities
- Open-set object detection without prior training
- Text-guided object identification and localization
- High-precision bounding box prediction
- Flexible query system using natural language
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to perform zero-shot object detection without requiring labeled training data for specific objects sets it apart. It can detect new objects simply by providing text descriptions.
Q: What are the recommended use cases?
The model is ideal for applications requiring flexible object detection, such as content moderation, image analysis, and automated visual inspection systems where the objects of interest may not be known in advance.
