Hit the industry directly | the flying propeller launched the industry's first model selection tool

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Strike directly at the industrial landing | The propeller launched the industry's first model selection tool

With the development of technology ,AI Algorithms have gradually penetrated into all walks of life .AI The algorithm is efficient , But in real projects , Developers often face many complex application scenarios . There are hundreds of open source algorithms in the industry , Hardware is becoming more and more diverse . How to do it in a specific scenario , Quickly select the most suitable AI Algorithm and matching 、 The most cost-effective hardware , It is a big pain point for industrial developers .

In order to quickly solve the problem of model and hardware selection , Enable developers to do more quickly AI Project landing , Flying propeller The team launched **「 Scene model selection tool 」**. It considers the real industrial landing demands of users , And integrate the Flying propeller Industry practice experience accumulated by the team for a long time . You can recommend appropriate models for users' real scene needs 、 Optimization strategy and hardware combination . For typical scenarios , It also recommends relevant industrial practice examples .

Click to get the product experience address

https://www.paddlepaddle.org.cn/smrt

You can also visit directly Flying propeller Official website —— model base —— Use the industry model selection tool .

It's not difficult for a careful partner to find , There is also a very intuitive data analysis function in the model selection tool , Users only need to upload their own annotation files （ The original drawing is not required ）, Tools can analyze data characteristics , Provide model selection and optimization strategies . The current model selection tool supports Labelme、 Elf label 、labelImg And other mainstream annotation software , Support at the same time voc data format 、coco Data format and seg（ Semantic segmentation ） data format .

Such a good tool , How to use it more efficiently ？ We use an actual case of industrial quality inspection , Explain in detail for everyone .

Case explanation

In the defect detection project of a steel plant , The user uses the linear array camera to detect the defects on the steel plate , Control by encoder , Every time 4000 Line generates a sheet 4096*4000 Size image . According to the project operation requirements , It is necessary to accurately calculate the area of the defect , At the same time, it needs to be in 2080Ti On the video card 200ms Complete defect detection .

So how to determine the final model through the model selection tool ？

Step one ： Determine the possible cropping scheme according to the image size

Generally, the image size obtained by linear array camera is large , But in practice , They are often cut into small sizes for training and prediction , And how many pieces are cut , What is the net size of each picture , It determines whether the final model can complete the identification task within the specified time .

Step two ： Query through the model selection tool

Choose the right model

Because the project needs to accurately identify the area of defects , Therefore, the project selects a series of image segmentation models . Under the specified time conditions , According to the number of segmented images, the maximum prediction time of each image can be calculated , Query the corresponding model in according to the model selection Input-size（ The size of the image after cropping ） Combination meeting the prediction time requirements under the same conditions , choice Target-size（ Actual network access size ） The largest set of values , Finally, the appropriate model combination is selected .

remarks ： At present, the scheme mainly considers the prediction of concatenation sequence after image segmentation .

Step three ： Based on the selected model

Model optimization

Label the image according to the final cutting size of the model , According to the data analysis function in the model selection tool , Further analyze the characteristics of the data , In view of the unbalanced distribution of its samples , Deep optimization by updating the loss function .

[ Failed to transfer the external chain picture , The origin station may have anti-theft chain mechanism , It is suggested to save the pictures and upload them directly (img-fh8Px9Yc-1656417796310)(https://mmbiz.qpic.cn/mmbiz_png/sKia1FKFiafghbw8eea7DHWROKNyS5wjUOUDtbefLasbuHKPsbrUZsOfD5JFk3wJxic5o6rzWWGd0lNRxYBRmbNcw/640?wx_fmt=png)]

The project is to select the corresponding model based on the known recommended hardware , If the user needs to select the hardware , The model selection tool also supports the automatic recommendation of matching hardware devices according to the time entered by the user . As shown in the case below , The time reserved for model reasoning is 50-100ms, The user enters the corresponding condition , You can get the recommendations of different hardware and the specific model running time in this period .

at present Flying propeller The team will deploy according to the needs of users , Offer based on 1660Ti、1080 Ti、2080 Ti、3090 Wait for a variety of chips in TensorRT FP32 Test data for , In the future, more cloud side deployment hardware will be supported , So as to better meet the landing needs of users .

Surprise benefits

It's so easy to use 「 Scene model selection tool 」, What are you waiting for ？

• Welcome to click and experience ：

https://www.paddlepaddle.org.cn/smrt

• Welcome to Join the user communication group , Just join the group Get intelligent manufacturing 、 A big gift package for smart city courses .

Focus on 【 Flying propeller PaddlePaddle】 official account

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