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OK.

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So welcome to twenty one point three.

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Well actually talk about and tell you how to go about creating a custom tensor for up to detection module

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model.

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So training the tens of the action the training process of tensile flow of detection is honestly a bit

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messy and this is probably one of the best of the detection Well most mature object libraries on the

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market today market.

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But an open source market you can say and it's explained fairly well it actually is bit tricky to use.

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So this is a step I broke down from looking at it.

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So first we prepare a data set in the T.F. record format.

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That's a specific record.

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I'll show you in the next few slides.

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Then we need to create a class label file that's a dog P.B. text file.

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Then we need to download a pre-trained Kokoo model and we need to set up the correct file that actually

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structure configured the object action pipeline and then start training.

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So let's go step by step.

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So what does it T.F. record format.

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So it tends to flow because the action expects so it treating and test data to be in this T.F. or the

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school record format.

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This is pretty much what it looks like here.

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Luckily though we can convert existing data sets that's like in the past SQL View see data set which

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is stored in Exod. Mosha directly to ATF records file by using the script.

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They provide this for the here.

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So it's quite myself.

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And no we talk about a classily files.

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So basically this is what the castable file looks like.

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It's basically just on a dictionary Shukria where we have the IDs and label names.

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So that's pretty much what we need to do with redefining.

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If we're training a detector to detect let's say London underground tube signs you'll just put the name

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of the class here and it's I.D. and you keep adding more and more items all objects here.

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So now we have to use pre-trained model.

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OK.

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So we don't train this model and this is like the resonant model that we use in the previous chapter.

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So we don't have one here.

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So tensile flow who has several pre-treat models and one Ko-Ko and Coko is basically a large scale object

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protection segmentation and captioning data set and basically has a lot of features here.

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You can go to the Web site this Cocco decision it and you will find it.

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And so you don't live with the models here.

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This link carries you to all the models that are available on tensor.

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And basically just use it downloaded onto mental health and on target here.

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So that's what we do.

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And this is a list of all the models of healable here.

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It gives the speed and the map scope which is quite useful helps you choose which model is most appropriate

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for your application.

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So now we get to configure object detection pipeline said abjection pipeline configuration file is composed

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of five sections.

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Basically we have a model that we define here to configuration here.

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Then we have the training config here.

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So we add this through here and the in part Real talk about soon the valuation config and evaluation

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in podrida.

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So let's take a look at this file in more detail.

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So this is a sample of the model file the model section here.

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What it looks like is this is the model config.

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I should say.

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OK.

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So we basically what we have to note is that we have some templates already that we can use inside of

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of flow.

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So we just basically make sure that all classes match up to the classes that enable custom are being

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attacked to Dexter.

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So this one was taken from the president one on one.

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And those Treen in the Pascrell VRC dataset.

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So as I said you don't need to rewrite this file just edit to one belonging to the preacher and model

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to be using Dymo defines the model defines all unnecessary.

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Our Or our CNN and as the parameters and when using pretreated models as best we leave this configuration

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configuration file unchanged unchanged just a bit.

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This red box here with the classes and notices that trade input Greedo and the yvel config and involved

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in podrida sections.

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So this is what we have to try and change these in red.

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Basically directory mappings.

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So we have to make sure that they're actually correct and pointing to the correct files that you want

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to use.

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This will be your label file.

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This will be your record file you're treating UTF record file.

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This is what disappoints do here.

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So for validation and for record for training and for validation.

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And this is Leavell parts of boat as well.

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OK.

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So remember the labels are the same ideas will be the same it's the same file.

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OK.

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So now here's the directory structure of the project.

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So this is how and where we put our files.

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So the PBX ex-felon label file that goes inside a directory called data.

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Then we have a record files which is a train and evaluation of record files or validation whatever you

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want to call it same thing.

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And then we have a new directory here models and then we have subdirectory model here.

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This is where we put our pipeline config file.

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That's this file here.

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And then we just have the trained directory and the evaluation directory here as well under the model

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inside the model form which is inside of the models for the here.

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So now this is how we start the training process.

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So we go to basically terminal and we just copy this line of code here and make sure to code the lines

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in a read here.

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These correspond to the model you are using and the directory that we just created here.

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OK.

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This is the day that actually it needs to be pointing to and it doesn't do that.

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And then you can actually bring up tents board to monitor your treating progress which is pretty cool.

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It's going to be district directory here.

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That again does your data directly at the specified here.

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And then before that I should have mentioned this earlier but it's important when you're labeling it

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images use a software that actually produces it in the correct format.

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So this is how we use annotations here.

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So this is my wife with my dog Samuel and software we use as a label.

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AMG I think this is two elves they have here this really label I am the label image obviously.

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So download it if you want to do it.

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It's available for Windows Mac and Linux.

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And this is the format that Pascal VRC Exham a format that we use now.

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It is not what we used us but we generate the image anti-Chavez in this format using the software.

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It actually does it automatically for you.

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And we can use our tents for a script I mentioned earlier to convert this file directly back to the

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text of the record files.

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So this is a summary here.

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We didn't do a full project here for the following reasons.

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Training and SSTO even a faster are CNN One is you is very impractical.

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It is going to take forever.

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So you definitely need a GP or a cloud to use to effectively train this A.F. all the data sets are there

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huge there are quite a few gigs of storage and also to setting up a GPU when a local system is a nightmare

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sometimes.

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It's a very scary task but once you get it working it's good you feel very happy because it's so much

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faster.

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So I've of all time general steps here.

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The Trina's model there are also some good tutorials I found online that do this as well.

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Basically try to make it as simple as possible if you going through all the steps telling you what to

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pay attention to and what's important.

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I've actually tried this on my system as well so I know what works.

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So I wish you all the best of luck when making your own object to actual text detectors.

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Thank you.