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所在平台: Udemy |
课程主页: https://www.udemy.com/course/instance-segmentation-with-python/
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课程名称：使用深度学习实时进行视频分割的Python 课程概述： 本课程引导您步入计算机视觉的动态世界，学习如何通过Python和深度学习掌握视频实例分割的艺术。视频实例分割是在像素级别上检测和分割各个对象的任务，与语义分割不同，后者只为每个像素分配一个类标签。通过本课程，您将解密移动图像中的隐藏信息，掌握动态叙事的技巧。无论您是初学者还是科技爱好者，本课程都将帮助您深入了解视频分析的复杂性，揭示深度学习在动态视觉叙事中的应用。 实例分割不仅识别对象位置，还能够准确描绘对象的形状，是精确定位和识别具体对象实例的关键。该任务在自动驾驶及其他领域具有重要的实际应用。 课程重要性： 理解视频实例分割是当前技术创新的前沿，超越了简单的对象检测，提供了关于每个对象的运动和形状的像素级理解。这项技能在机器人、自动化系统、医疗健康、娱乐等多个行业中都是至关重要的。 课程应用： - **监控与安全**：提高安全系统的准确性，掌握视频实例分割进行精确的对象识别。 - **自主系统**：特别适合那些注重精准对象追踪的自驾车和无人机应用。 - **医学影像**：通过视频序列的像素级理解，支持精确的定位和追踪以用于诊断。 - **娱乐行业**：在视频中应用详尽的对象分割技术，制作出具有视觉吸引力的效果。 课程主要目标： - 实时的视频实例分割流程：使用Python和PyTorch进行实时视频实例分割。 - 构建、训练和测试深度学习模型，部署到个人项目中。 - 了解YOLOv8和Mask RCNN的深度学习架构。 - 定制车辆数据集的配置及其注解，以便进行实例分割。 - 设置训练实例分割模型的超参数。 - 在自定义数据集上训练YOLOv8和Mask RCNN模型，并在视频和图像中测试训练好的模型。 - 实现对汽车、摩托车和卡车的实例分割。 结课程目标： 通过本课程，您将能够在实际应用中，对自定义数据集执行实时视频实例分割，掌握Python和深度学习框架的实用技能，为各行业带来极大的市场需求。将您的热情转化为专业知识，成为解读动态图像像素语言的视觉叙述者。 快来加入这个学习之旅，探索Python、深度学习与视频实例分割的结合！别错过这一变革性经验的机会，立即注册，提升您的技能！
Introduction: Step into the dynamic realm of computer vision and get ready to be the maestro of moving pixels! Dive into the world of 'Video Instance Segmentation with Python Using Deep Learning.' Unleash the magic hidden in each frame, master the art of dynamic storytelling, and decode the dance of pixels with the latest in deep learning techniques. This course is your passport to unlocking the secrets hidden within the pixels of moving images. Whether you're a novice or an enthusiast eager to delve into the intricacies of video analysis, this journey promises to demystify the world of deep learning in the context of dynamic visual narratives.Instance segmentation is a computer vision task to detect and segment individual objects at a pixel level. Unlike semantic segmentation, which assigns a class label to each pixel without distinguishing between object instances, instance segmentation aims to differentiate between each unique object instance in the image. Instance segmentation is a computer vision task to detect and segment individual objects at a pixel level. Instance segmentation goes a step further than object detection and involves identifying individual objects and segment them from the rest of the region. The output of an instance segmentation model is a set of masks or contours that outline each object in the image, along with class labels and confidence scores for each object. Instance segmentation is useful when you need to know not only where objects are in an image, but also what their exact shape is. So, Instance segmentation provides a more detailed understanding of the scene by recognizing and differentiating between specific instances of objects. This fine-grained recognition is essential in applications where precise object localization is required. For example In the context of autonomous vehicles, instance segmentation is valuable for understanding the surrounding environment. It helps in identifying and tracking pedestrians, vehicles, and other obstacles with high precision, contributing to safe navigation.Deep learning is one of the most effective approach to Instance segmentation, which involves training a neural network to learn complex relationships between pixels and able to learn rich feature representations. The goal of Instance segmentation is to train a Deep Learning model which can look at the image of multiple objects and able to detect and recognize individual objects at pixel level. In this course, you will perform real time video Instance segmentation with latest YOLO8 which is a deep CNN and you will also do instance segmentation using Mask RCNN which is a region based CNN.Importance: Understanding video instance segmentation is at the forefront of technological innovation. It goes beyond mere object detection, offering a pixel-level understanding of each object's motion and shape over time. The importance of this skill extends across industries, influencing advancements in robotics, autonomous systems, healthcare, entertainment, and more.Applications:Surveillance and Security: Contribute to the development of advanced security systems by mastering video instance segmentation for accurate object identification.Autonomous Systems: Enhance your skills for applications like self-driving cars and drones, where precise object tracking is crucial for decision-making.Medical Imaging: Dive into the medical field, where pixel-level understanding in video sequences aids in precise localization and tracking for diagnostic purposes.Entertainment Industry: Join the league of creators in the entertainment industry, mastering the art of visually engaging effects through detailed object segmentation in videos.Course Key Objectives:In this course, You will follow a complete pipeline for real time video instance segmentation:Real-Time Video Instance Segmentation with Python and Pytorch using Deep LearningBuild, Train, & Test Deep Learning Models on Custom Data & Deploy to Your Own ProjectsIntroduction to YOLOv8 and its Deep Learning ArchitectureIntroduction to Mask RCNN and its Deep Learning ArchitectureVideo Instance Segmentation using YOLOv8 with PythonInstance Segmentation using Mask RCNN with PythonConfiguration of Custom Vehicles Dataset with Annotations for Instance SegmentationHyperParameters Settings for Training Instance Segmentation ModelsTraining Instance Segmentation YOLOv8 and Mask RCNN Models on Custom DatasetsTesting Instance Segmentation Trained Models on Videos and ImagesPerform Car, Motorbike, and Truck Instance SegmentationDeploy Trained Instance Segmentation ModelsSo, Are you ready to take your understanding of deep learning to the next level and learn how to apply it to real-world problems? This course is especially designed to give you hands-on experience using Python and Pytorch to build, train and Test deep learning models for Instance segmentation applications." At the end of this course, you will be able to perform real time video instance segmentation to your own real word problem on custom datasets using Python. Acquire hands-on experience with Python and deep learning frameworks, gaining a skill set that's in high demand across industries. Become a visual storyteller, interpreting the language of pixels in moving images. Seize the opportunity to be at the forefront of technological advancements and make a lasting impact in fields where video analysis is the key to unlocking the future.Embark on this learning journey, where the fusion of Python, deep learning, and video instance segmentation awaits your exploration. Don't miss your chance to be a part of this transformative experience. Enroll now and turn your passion into expertise!
