Supercharging Small Models with Grounding DINO, SAM, and AutoDistill for Dataset Creation
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In this thrilling adventure, we’re diving into the world of small yet mighty models for object detection and image segmentation. Our goal? Harness the power of large models to create efficient, high-quality datasets that can train faster, smaller models without compromising on performance. Let’s get started!
The Journey Begins 🗺️
This article is for those who are ready to build their own datasets using state-of-the-art models/tools like Grounding DINO, SAM, and AutoDistill. If you’ve ever been frustrated by slow models or the hassle of manual annotation, fear not! We’ll automate the process of generating labeled data and refine it using Roboflow to ensure quality.
1. Introduction to Dataset Creation
Creating a high-quality dataset is the foundation of any successful machine learning project. In this section, we’ll explore how to leverage large models like Grounding DINO and SAM to label images automatically. We’ll also refine those labels with tools like Roboflow, allowing for a smooth and efficient workflow.
In this adventure, we’ll focus on the essential steps:
- Grounding DINO for automatic detection based on text prompts
- Segment Anything Model (SAM) for precise image segmentation
- AutoDistill to streamline dataset creation
- Roboflow for label improvement and augmentation
1. Grounding
Grounding connects AI knowledge to real-world examples, improving accuracy and reducing errors, especially in complex situations. In computer vision, it links text descriptions to specific image elements, helping machines interpret visuals using both language and images.
Visual Grounding (VG) aims to locate the most relevant object or region in an image, based on a natural language query
2. DINO
DINO (self-distillation with no labels) is a self-supervised learning method by Facebook/metaAI for computer vision. It teaches itself by comparing different versions of the same image without human-labeled data, using a teacher-student method to recognize patterns.
3. Grounding-DINO
Grounding DINO extends DINO with language capabilities, allowing it to detect and locate objects based on text descriptions. It excels in tasks like open-set object detection and language-based queries.
- How it Works:
- Encodes both image and text prompt
- Combine these to generate a bboxs
4. Segment Anything Model (SAM)
SAM, developed by Meta AI, segments any object in an image with just a click or a few points. It excels in applications from photo editing to scientific analysis. according to their GitHub repo, they didn’t implement text as input yet
- Key Features:
- Zero-shot segmentation of unseen objects
- Can be prompted with points, boxes, or text
- Real-time mask generation
- Handles object occlusion and overlap
- How it Works:
- Encodes both image and prompt (e.g., point or box)
- Combine these to generate a mask
