Object Localization is the task of locating an instance of a particular object category in an image, typically by specifying a tightly cropped bounding box centered on the instance. In this 2-hour project-based course, you will be able to understand the Object Localization Dataset and you will write a custom dataset class for Image-bounding box dataset. Additionally, you will apply augmentation for localization task to augment images as well as its effect on bounding box. For localization task augmentation you will use albumentation library. We will plot the (image-bounding box) pair. Thereafter, we will load a pretrained state of the art convolutional neural network using timm library.Moreover, we are going to create train function and evaluator function which will be helpful to write training loop. Lastly, you will use best trained model to find bounding box given any image.
Deep Learning with PyTorch : Object Localization
Deep Learning with PyTorch : Object Localization
位教师：Parth Dhameliya
访问权限由 Coursera Learning Team 提供
6,703 人已注册
您将学到什么
Create custom dataset for Localization problems
Apply augmentations for localization task and load pretrained model
Create train function and evaluator for training loop
您将练习的技能
要了解的详细信息
添加到您的领英档案
仅桌面可用
了解顶级公司的员工如何掌握热门技能
在 2 小时内学习、练习并应用岗位必备技能
- 接受行业专家的培训
- 获得解决实训工作任务的实践经验
- 使用最新的工具和技术来建立信心
关于此指导项目
分步进行学习
在与您的工作区一起在分屏中播放的视频中，您的授课教师将指导您完成每个步骤：
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Set up colab run environment
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Configurations
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Understand the dataset
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Augmentations
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Create Custom Dataset
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Load dataset into batches
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Create Model
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Create Train and Eval Functions
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Training Loop
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Inference
推荐体验
Prior programming experience in Python and basic pytorch. Theoretical knowledge of Convolutional Neural Network and Training process (Optimization)
10个项目图片
位教师
提供方
学习方式
基于技能的实践学习
通过完成与工作相关的任务来练习新技能。
专家指导
使用独特的并排界面，按照预先录制的专家视频操作。
无需下载或安装
在预配置的云工作空间中访问所需的工具和资源。
仅在台式计算机上可用
此指导项目专为具有可靠互联网连接的笔记本电脑或台式计算机而设计，而不是移动设备。
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