grounding dino base

Introduction

The Grounding DINO model is designed for zero-shot object detection, enhancing closed-set detection models with a text encoder to enable open-set capabilities. It achieves notable performance metrics, such as a 52.5 AP on the COCO zero-shot benchmark.

Architecture

Grounding DINO integrates a text encoder with a traditional object detection model, allowing it to detect objects without pre-labeled data. This open-set detection capability is a significant advancement over previous models.

Training

The model is trained using a grounded pre-training approach that leverages both image and text data. This methodology enables the model to generalize to unseen objects by understanding contextual cues from textual descriptions.

Guide: Running Locally

1. Setup Environment: Ensure that Python and essential libraries such as torch, PIL, and transformers are installed.
2. Load Model: Use the AutoProcessor and AutoModelForZeroShotObjectDetection classes to load the model.
3. Prepare Input: Load an image and define text queries for detection.
4. Inference: Process the inputs and run the model to detect objects.
5. Post-process: Use the provided functions to extract and refine detection results.

For optimal performance, consider using a cloud GPU service like AWS EC2, Google Cloud Compute Engine, or Azure VMs with GPU support.

License

This project is licensed under the Apache 2.0 License, allowing for both personal and commercial use, modification, and distribution.