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Welcome to this complete Roboflow Annotation Tutorial, specially designed for AI & Data Science students who are working on Computer Vision and CNN projects. In this video, we focus on one of the most important steps in any computer vision pipeline — Image Annotation. Annotation is the backbone of supervised learning in computer vision. Without correct labeling, even the most advanced CNN model cannot perform well. That’s why tools like Roboflow are widely used in the industry to prepare high-quality datasets for object detection, image classification, and segmentation tasks. 📘 What You Will Learn in This Video What is Roboflow and why it is used Creating a Roboflow account and project Uploading image datasets Understanding annotation types (Bounding Box, Classification) Step-by-step image annotation using Roboflow Labeling objects correctly for CNN models Managing classes and annotations Dataset versioning in Roboflow Exporting annotated datasets for training Using Roboflow datasets with YOLO, CNN, TensorFlow, and PyTorch Best practices for accurate annotation 🎯 Why This Video is Important If you are working on: ✅ CNN Image Classification ✅ Object Detection Projects ✅ AI & Computer Vision Assignments ✅ Real-world ML / DL Projects Then this video will give you a strong foundation in dataset preparation, which is a must-have industry skill. 👨🏫 Course Information Course: AI & Data Science Instructor: Sir Nasir Hussain Institute: Saylani Z.A.I.T Park 🚀 After Watching This Video You will be able to: ✔ Create and manage annotated datasets ✔ Prepare data for CNN training ✔ Use Roboflow with real ML projects ✔ Avoid common annotation mistakes 📌 Don’t forget to Like, Share, and Subscribe for more practical AI & Data Science content.