图像中的Tensorflow计数对象 [英] Tensorflow Count Objects in Image

问题描述

机器学习的新手，因此正在寻找入门的方向.最终目标是能够使用Tensorflow训练模型以计算图像中的对象数量.我最初的重点是训练模型以计算一种特定类型的对象.所以可以说我拿硬币.我只会训练模型来计算硬币.不必担心为所有不同类型的对象创建通用计数器.我仅完成了Google对花朵的图像分类的示例，并且我了解了其中的基本知识.因此，寻找如何入门的线索.这是图像分类问题，我可以使用与鲜花...等相同的逻辑吗?

New to machine learning so looking for some direction how to get started. The end goal is to be able to train a model to count the number of objects in an image using Tensorflow. My initial focus will be to train the model to count one specific type of object. So lets say I take coins. I will only train the model to count coins. Not worried about creating a generic counter for all different types of objects. I've only done Google's example of image classification of flowers and I understand the basics of that. So looking for clues how to get started. Is this an image classification problem and I can use the same logic as the flowers...etc etc?

推荐答案

硬币问题的最佳性能解决方案可能是使用回归来解决此问题.用场景中的对象数量注释5k图像，然后在其上运行模型.然后，您的模型仅输出正确的数字. (希望如此)

Probably the best performing solution for the coin problem would be to use a regression to solve this. Annotate 5k images with the amount of objects in the scene and run your model on it. Then your model just outputs the correct number. (Hopefully)

另一种方法是对图像是否显示硬币进行分类，并使用像这样的滑动窗口方法: https://arxiv.org/pdf/1312.6229.pdf 对每个显示硬币的窗口进行分类.然后，您对找到的区域进行计数.该代码更易于注释和学习，并且可扩展性更好.但是，您会遇到一个问题，即选择好的窗口并以简洁的方式使用这些窗口的结果.

Another way is to classify if an image shows a coin and use a sliding window approach like this one: https://arxiv.org/pdf/1312.6229.pdf to classify for each window if it shows a coin. Then you count the found regions. This one is easier to annotate and learn and better extensible. But you have the problem of choosing good windows and using the result of those windows in a concise way.

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