通过Grounding DINO、SAM和AutoDistill为数据集创建超级增强小模型
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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!
在这次激动人心的冒险中，我们将深入探索小而强大的对象检测和图像分割模型的世界。我们的目标？利用大型模型的力量创建高效、高质量的数据集，以便在不妥协性能的情况下训练更快、更小的模型。让我们开始吧！
The Journey Begins 🗺️
旅程开始 🗺️
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.
本文适合那些准备使用最先进的模型/工具（如 Grounding DINO、SAM 和 AutoDistill）构建自己数据集的人。如果你曾因模型速度慢或手动标注的麻烦而感到沮丧，不用担心！我们将自动化生成标记数据的过程，并使用 Roboflow 进行精炼，以确保质量。
1. Introduction to Dataset Creation
1. 数据集创建简介
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.
创建高质量的数据集是任何成功机器学习项目的基础。在本节中，我们将探讨如何利用像Grounding DINO和SAM这样的模型自动标记图像。我们还将使用Roboflow等工具来完善这些标签，从而实现顺畅高效的工作流程。
In this adventure, we’ll focus on the essential steps:
在这次冒险中，我们将专注于关键步骤：
- Grounding DINO for automatic detection based on text prompts
- Grounding DINO 用于基于文本提示的自动检测
- Segment Anything Model (SAM) for precise image segmentation
- Segment Anything Model (SAM) 用于精确的图像分割
- AutoDistill to streamline dataset creation
- AutoDistill 用于简化数据集创建
- Roboflow for label improvement and augmentation
- Roboflow 用于标签改进和增强
1. Grounding
1. Grounding
Gr...