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Description: Autodistill is a ground-breaking tool revolutionizing the world of computer vision! In this video, we will show you how to use a new library to train a YOLOv8 model to detect bottles moving on a conveyor line. Yes, that's right - zero annotation hours are required! We dive deep into Autodistill's functionality, covering topics from setting up your Python environment and preparing your images, to the thrilling automatic annotation of images. What makes Autodistill a game-changer is its ability to distill knowledge from large foundational models like GroundedSAM, transferring this knowledge into highly-optimized computer vision models. We're ecstatic to showcase how this library can turn hours of tedious manual annotation into a fully automated process, without compromising the accuracy of your models. Chapters: 00:00 Autodistill Overview 02:03 Project Overview 03:16 Setup Python Environment 04:46 Prepare Images 06:22 Autoannotate Images 07:50 Train YOLOv8 Model 08:28 Run Inference on Video 09:16 Conclusion Resources: - 🌏 Roboflow: - 🧪 Autodistill Repository: - 🌌 Roboflow Universe: - 📚 Roboflow Notebooks Repository: - 📓 How to Auto Train YOLOv8 Model with Autodistill Google Colab: - 🗞️ Autodistill blogpost: - 🎬 How to Train YOLOv8 Object Detection Model on a Custom Dataset YouTube video: - 🎬 How to Train YOLOv8 Instance Segmentation Model on Custom Dataset YouTube video: - 🎬 Detect Anything You Want with Grounding DINO YouTube video: - 🎬 SAM - Segment Anything Model Overview YouTube video: - 🎬 Accelerate Image Annotation with SAM and Grounding DINO YouTube video: Don't forget to like, comment, and subscribe for more content on AI, computer vision, and the latest technological breakthroughs! 🚀 Stay updated with the projects I'm working on at and ! ⭐
