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The world's first open source image recognition system is online! Face, goods, vehicle recognition all in one!

2021-06-24 00:30:30 Baidu brain

Fathers and elders , Villagers ! You know faces 、 goods 、 Vehicle identification , Search for pictures with pictures, even automatic driving , What's the technology behind it ?

At first, Xiaobian thought it was image classification 、 Target detection, these things , What's the difficulty ? But those who are proficient in these applications BAT Senior engineers make millions a year , I'm proud of my life !!!

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When Xiaobian is turning into lemon essence ,BAT The great God said : Behind this is the comprehensive use of target detection 、 Image classification 、 Measure learning 、 The key to image retrieval is 【 Universal image recognition system 】…

What is measurement learning ? What is image retrieval ? What is a universal image recognition system ?! It seems that it's still me Too Simple,Too Naive 了 …

I really can't make a million a year ?! When Xiaobian beat his chest and feet , Xiaobian suddenly found a general image recognition system to quickly build artifact !OMG! This dream will come true soon !

hurriedly Star Collect :https://github.com/PaddlePaddle/PaddleClas

So what's so amazing about this project , Image recognition is better than image classification 、 Where is the strength of target detection ?

take [ Commodity identification ] Take a chestnut , If you use pure image classification and object detection , You'll find that :

Tens of thousands of commodity categories : There's no way to put all the categories into the training set in advance ! The training set is not complete, how to train the algorithm ?
The sample categories are extremely uneven : The quantity distribution of each kind of goods is uneven , Some categories are just 1、2 A picture ! Even if such a class is put into algorithm training , The recognition accuracy is also very, very low .
Category update is very fast : Various businesses keep introducing new categories , Every time you add a new product, you have to work hard to retrain the model !
And using image recognition , Not only can the above problems be solved perfectly ! And it's very fast , Simple and easy to understand . When you have a picture of the object you want to recognize , Just three steps , Multiple categories 、 Small sample 、 Data imbalance is no longer a problem ! And it's not just product identification , It's also possible to carry out vehicle 、 Face 、Logo、 Pedestrian identification !!! Let's identify what we want to identify !( It's the feeling of freedom. That's right !)

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And this image recognition system 4 There are three core components , It's all carefully polished . Whether it's used alone or in series , They all have extraordinary effects :

Subject detection : With high precision and ultra light weight PP-YOLOv2 Detection algorithm , Fast subject detection of image , Improve recognition efficiency .

Backbone network : selected 6 Series Backbone, Covering the most sophisticated mobile model and high-precision server model , Support the rapid modification of the structure , Meet the needs of different scenarios .

Measure learning : Integrate ArcMargin, CenterLoss, TriHard And other leading measurement learning methods in the industry , And can be combined at will , It's easy to train robust image features .

Search system : Integrated Baidu self-developed Möbius Algorithm , Efficient vector retrieval , And can update the search database at any time , Long term use of one training .

Developers can not only assemble and use these four modules individually or independently , You can also use the built vehicle identification directly 、LOGO distinguish 、 Commodity identification 、 There are four systems for animation recognition . Just supplement the search database , It can be put into operation directly !

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With their help , Keep up with the programmers of big factories , A million a year is not a dream !( Gradually showing a rich smile ~ )

So strong 、 Project with heart , What are you waiting for ?! Not in a hurry Star Let's get in the car !

Portal :https://github.com/PaddlePaddle/PaddleClas

Quick experience :

https://github.com/PaddlePaddle/PaddleClas/blob/release%2F2.2/docs/zh_CN/tutorials/quick_start_recognition.md

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