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Hi guys.

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In this video tutorial we will see how we can use Adobe Air for personal protective equipment detection.

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So the dataset I will be using in this project is available on Roboflow.

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Publicly, the dataset consists of 3235 images and let us show you the dataset.

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So we have seven different classes which are marked as zero 11 three four, five.

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We will change the name of this classes.

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For example, in the class number zero, it's handmade.

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So we will change the name of the class zero to helmet the class 11, which is the shield.

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You can see here the person is wearing a shield.

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So it's detecting that the person is wearing the shield.

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So we will change the name of this class from 11 to shield.

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And in the third class, we can see that the person is wearing a jacket.

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So we will change the name of this class from three to jacket and plus we can further we can see the

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class for the model is just detecting the mask.

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So we will change the noun name of this class from four to mask.

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In the same way we will change the name of other classes as well.

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So like in this case, we can see that in the ninth class the model is detecting that it's the class

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of boots.

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So we will change the name of of this class from nine to boots.

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Okay.

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So in this way we will rename all the classes so that when the model detects handmade jacket or boot.

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So we should know that the model has detected a jacket, helmet or both instead of it should appear

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in the detected object, it is zero one 2 or 9.

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Okay.

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So we don't want a numeric number.

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We should we want the name of the class.

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Okay.

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So let's start towards the implementation.

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But before going towards the implementation, let us see the model of health check.

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Well, we can see that we have a total of 3235 images and we have each image is contains around in average

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4 to 5 bounding boxes.

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So in each image we are detecting 4 to 5 different classes or objects.

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So we have around 14,341 information in 3235 images, and each image contains around 4 to 5 bounding

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boxes.

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And that image size is average.

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Size is 4416, cross 416.

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But we will resize the image to 640 Cross 640 because our V8 model is trained on 640 cross 640 images.

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So it's always better to keep that size.

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And here we can see that the class balance so we can see that these three last three classes are unbalanced

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so that that data set is not very much balanced.

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You can see that some classes are imbalanced or you can say that some classes are underrepresented,

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which include class four, six and 11.

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Okay.

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So if the data set, we can see that we have 2300 images for the training set, 647 images for the validation

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set and 324 images in the train testing set.

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So our split ratio is 70, 2010.

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70% is for the training, 20% is for the validation set and 10% for the testing set.

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Okay.

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So to import this model.

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Into our basic CoLab notebook.

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We will click on download and just show down the up format.

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So basically we will select YOLO V5 PyTorch because Ultralytics has also basically invented Yolov5 PyTorch.

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So if we choose Yolov5, pytorch or V8, it's the same.

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So.

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So just click on show, download ebook and just copy this from here and just click and copy.

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So now we will just paste this code into our Google CoLab notebook.

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And in this way we will export this dataset from here into our Google CoLab notebook.

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So before running the script, please make sure that you have selected the runtime as GPU.

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Okay, now we will click on import OS, basically import OS because we are using OS to create a helper

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variable over here so that we can navigate to different files dataset easily.

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We're just running this title first, then we are using Glob is used to return all file paths.

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Like if you want the input images file path, we can easily return using glob library.

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Then we are importing image and display.

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Basically, we are using these two libraries to display any output image like predicted output, image

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or any input image into our Google CoLab notebook.

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So it will display any output or input name image into our Google CoLab notebook.

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We require image and display library and using display dot output function.

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We are basically this function is used to clear output in the notebook.

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So if you want to clear output in the notebook we use via dash output function.

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Okay, So just okay, so we can leave it for now as well because.

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Because currently we don't have any output, so we don't need this.

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So in the first step we need to check whether we have access to GPU or not.

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So we'll just run this cell.

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And it will show us that whether we are using or not so GPU memory uses.

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So that's fine.

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So we are defining our basically run directory here.

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So this is our home directory which is over here.

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This all is our home directory.

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So now we will install a credits using PIP store.

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Basically it can be installed in two ways from the source phone or via pip.

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So V8 is the first version of YOLO, the first iteration of YOLO, which has its own optional package,

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then YOLO.

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V7.

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They don't have their own official package, so YOLO behaved as its own official package.

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So use installing ultralytics.

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We can install YOLO and if you use PIP install ultralytics it will install the V8 version.

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So if you do PIP install it will install the YOLO V8 version.

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So there is another way you can install or implement YOLO V8 is to clone the github repo of your ultralytics

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yolo v8 GitHub repo.

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So if you want to clone the implement using by cloning the GitHub repo, you can also do this.

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Here is the code written to clone the GitHub repo.

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So what will follow the easy way, which is the easy way is used to install ultralytics.

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We use to clone the github repo in that cases where we need to make some changes in the code.

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For example, I want to add the speed estimation in the script in my prediction.py file so that in that

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case I will go on the github repo where I need to make some change in the predict.py with a train.py.

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In this case I don't need to make change in the predict.py or the train.py or the validation script.

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So in the current script, straight in the current project we are not making any change in the predictions.

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We have training, script or validation step, so we are not cloning the GitHub repo.

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If need to add some code in the training validation or prediction step then will clone the GitHub repo.

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So I'm using PIP install activities to install the latest version of YOLO which is YOLO V8.

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So this will install the latest version of YOLO, which is YOLO.

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So it might take some seconds.

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Okay.

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So now we will import ultralytics to check whether our YOLO model installed and it's working fine or

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not.

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If it's not working, fine.

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And see what's the issue.

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But it's working fine over here.

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So.

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Okay, now.

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Now we will import the data detection dataset.

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This dataset from Roboflow.

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So first we will create a folder over here.

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So what choice is click on new folder or.

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So create a folder here, but we will create a folder using mkdir and the name of the folder is datasets.

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Okay, so you can change the name of the folder as well.

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So now you can see over here we have a folder by the name of datasets.

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Okay, this is an empty folder, but we will download the dataset into this folder.

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Okay.

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So just seeing what is our present working directory now setting the current directory as this datasets

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folder so that we can download the dataset directly into this datasets folder.

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So just copy this from here and just remove this and just paste this over here.

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So now you will be able to download the personal protective equipment of dataset from Roboflow to your

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Google CoLab notebook.

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So the dataset is being downloaded from here, so it might take some time.

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Okay, so.

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It's downloading and it's around 6%, 7%, because that is consist of around 303,255 images.

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So it will take some time to download, so please bear with me.

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And this is the datasets get download.

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So it's at 42% currently and 5,450% 5,560%.

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So in this way we will download our dataset completely, but it will take some time.

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Okay, it's 90 and it's 99%.

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Okay.

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The dataset is downloaded over here and we can see, okay, so if you happen some slide, you then just

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need to reload this because if I click over here a lot of files appear, so it just need to reload it

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and it will work fine.

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So don't need to worry in any case.

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Okay.

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So just wait for few seconds and here is our datasets folder and here is our detection datasets which

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consist of train test and validation.

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Okay?

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And this is our data file which consists of nine different seven different classes.

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Okay.

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One, two, three, four, five, six, seven different classes here.

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Okay.

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So first we just need to do one thing.

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I have told you that we will rename this classes by their names of the object, so I have done this.

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So let me upload that updated file data dot yml file over here.

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So just give me a minute.

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I will upload this.

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So, guys, you can see over here, this is my updated data dot yml file.

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So you can see that we have a class name against each class, like protective helmet, shield jacket,

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dust mask, eyewear glove, protective boots.

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So it's not a number.

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We have a class name against each class.

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Okay, so we have downloaded the data set and just checking we the data set location over here.

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Okay.

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So.

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So basically, if you want to train, validate and run inference or model and you don't need to do any

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modification in the code, for example, don't need to add any speed estimation or tracking or any other

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step, then you use the command line interface is the easiest way to do it.

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So we are currently using the command line interface to implement the training, the validation and

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testing of our model.

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Okay.

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So if you want to do detection, you can select is equal to detect.

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And if you want to do classification, you can select cross is equal to classifiers.

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For segmentation, you can select class is equal to segment.

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But we added to doing detection over here.

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So we have selected task is equal to detect and what is equal to train because we have first training

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our model and here we have chosen the YOLO V8 medium model and so on.

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Okay, so.

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Before we start the training.

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We just need to make few changes over here.

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We just need to rename this folder and just click one enter over here and just open the data dot yaml

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file.

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Okay.

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And just open the data dot html file.

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And just.

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Just go to train and just copy part.

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And just paste it over here and just go to valid copy this path.

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And just paste it over here.

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Okay.

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And just save it.

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Okay, so just click on CTRL s.

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And now we are training our model for 90 epochs and we are taking our image size as 640.

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And here is our data dot yml file path over here and let's run the training.

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So first we are downloading our model.

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So the training will take around 2 to 3 hours, but we will stop the recording and we will be back when

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the training completes.

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Okay.

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Well.

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The training has started.

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You can see over here.

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The training has started, but it will take quite some time.

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So we will pause the video as the training gets started so you can see that the training has started.

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So as the training complete, I will be back and explain you the rest of the code.

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Till then, see you then when the training computes.

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Guys the training of the model has completed.

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We have trained our model on 90 epochs with image size 640.

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And here are the training results we get.

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So in total, we have trained our model on 90 epochs.

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So it took around three hours for the training to complete.

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And we get we have got the best weights file and the 90th epoch last weights file as well.

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Okay, so we have seven different classes, protective helmet, shield jacket, dust mask and gloves

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and protective boots.

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So here we got the mean average precision with IOU 50 and mean average precision.

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When IOU varies from 50 to 95% for each of the class.

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So we can see that we have a very good for all the classes like it's around 97.8%, 66.1% and 92.8%

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for the jacket class and for 96.8% for the dust mask and IOU.

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When IOU varies from 50 to 95 with mean average precision.

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So we can see that here.

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We have also got very poor results as well.

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So we have a stored our results in the runs detect train to.

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And here we can see that here is the word weights file.

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And here we have the F1 curve to check what different files or results we have in this folder.

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Just run this cell.

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So you can see that we have the confusion matrix.

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So confusion Matrix basically tells us how our model handles different classes.

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We have the F1 curve, precision curve, precision recall curves, and then we also have the recall

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curve, and then we have the results in the form of CSP.

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So results dot CSV file show the performance of the model on each of the epoch in the results dot.

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We have the training and validation losses and then we have the model predictions on the validation

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batches as well.

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Okay, so let's first see what the confusion matrix we are getting.

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So confusion matrix is the chart that shows how our model handles different classes.

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So if we consider a jacket, for example, so we can see that 92% of the time our model detected correctly

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that a person is wearing a jacket, while 1% of the time we get the bounding box.

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But the jacket is incorrectly classified as the ideal, while 7% of the time our model will be trained

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fine tune.

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Your model for detection is unable to classify that the person is wearing a jacket or model is unable

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to detect that the person is wearing a jacket.

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So a model does not detect anything, although the person is wearing the jacket.

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Okay.

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So you can see that while 7% of the time when person is wearing the jacket, the model is unable to

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detect it.

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So in this way we can get information from the confusion matrix.

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Here we can see that 96% of times our model has detected correctly that a person is wearing a helmet,

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while 4% of the time when the person is wearing a helmet, our model is unable to detect the helmet.

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So here we have the graph with the training and validation loss.

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So we have the graphs of training and validation loss in the results dot PNG.

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So we can see that the loss value is continuously decreasing.

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So if we train this model on 200 to 50 epochs, we can get some more better results.

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While you can see that mean average precision is continuously increasing and recall is also getting

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better.

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So here you can see that mean average precision curve is continuously increasing with 50 and 50 to 95.

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So our results are quite good.

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So here are the model predictions on the validation batch.

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So these images are not used for training.

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So it's always better to have a looks like here model is just detecting that person is wearing the dust

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mask here.

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The model is detecting that the person are wearing gloves on both of his hands and the person is also

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wearing protective boots as well.

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So the model is working quite fine.

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Like here is not wear a glove.

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So here's the model has not detected that a person has a glove while the person has.

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Here we have been wearing a glove.

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So model is able to detect that the person is wearing a glove.

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Okay.

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So let's validate the model or validate our custom order.

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So we are taking the best weights which we got over here, best dot, and we will validate our custom

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model.

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So similarly as before, we are using command line interface to do the training.

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So we will be using command line interface to validate the model.

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So previously we utilized light mode is equal to train.

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So here we will write mod is equal to validate and we are performing detection.

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So DOS is equal to detect.

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And here we are just passing data dot yml file path, which basically data dot file contains our training

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set testing and validation test images path.

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Okay, so here we are validating our custom model so we can see that we also got very good result in

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terms of mean average precision with 50 and mean average precision when varies from 50 to 95.

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So all the results are quite good.

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So here we are doing inference machine with custom model.

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So inference means a prediction that we can run on image to detect a label, whether it is classification

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or a bounding box or a segmentation.

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So here we are testing model on the test dataset images.

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So here I pass that path for the test dataset images for example.

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Let me show you.

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Here we have the data set and here we have the test images.

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So we have just copied this path and just paste it over here.

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Let me do it again.

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Here.

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I've just had it.

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Wait.

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I have just added the part of my test dataset images and this is the path of my best weights file which

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am added over here.

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You can see and I am not doing prediction, so I am just change more to prediction.

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Previously I was doing validation.

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So I've written where when I was doing training.

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So I was written train and the task is detect.

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So now we have been detection of object detection.

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So I have written the text detect, okay, so just run this cell and it will a test on model on that

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test dataset images.

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So it might take few seconds to run.

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Then we will see what the results do we get.

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So please wait for two seconds until it drains completely.

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Then we will see what results we are getting.

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So the model has run on the test dataset images.

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So they are 324 images and the results are saved in the prediction.

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Predict eight file.

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As the results are saving run detect predicted.

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So just copy this path and just paste it over here.

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So as we have total 324 images, so we will not display all the 324 images.

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What results do we get on all the 324 images?

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Instead, we will only check the results on the first five images just sitting over here.

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So here to display output on the Google CoLab notebook I have imported from IPython dot display import

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image display.

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So just run this cell now and see what results do we get.

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So it might take few seconds, so please wait.

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So here you can see that our model is able to detect correctly the protected boots, the jacket, the

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gloves, dust masks, the protected helmet.

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So results are very wonderful here.

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The model is also able to detect the jacket, protective helmet.

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And here the model is also able to detect the protective helmets which are all below the gloves, everything.

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And here the model is also able to detect the dust mask and the protective helmet as well.

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So the results of the model are quite impressive.

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Guys, let's test our model on some demo videos and see how our model performs on the demo videos.

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So here I am downloading a demo video directly from my Google Drive.

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So just run this cell.

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The name of that video is demo dot MP4, so it might take few seconds to download.

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The video is downloaded.

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So now let's test our model on this demo video.

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I have passed my best model weights over here.

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I have set the confidence to 0.25 cause we are performing object detection and we are doing prediction.

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And here is that demo video path.

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So just run this cell and see what results do we get.

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So it might take few seconds to run this demo video.

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So because it will process the video frame by frame so we can see that the model is detecting the protective

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helmets, jackets and thus frame rate is 27.2 millisecond.

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Okay.

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So this 70, 80, 90, this is a progress.

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We are using GPU so it's a bit fast.

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Then CPU CPU in CPU, it takes quite some time to put to training and to do prediction.

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So it's always better to do a test.

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Our model on GPU and Google CoLab offers some free GPU as well.

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So I'm using Google CoLab free GPU.

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Okay guys.

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So.

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Might take some more time, although we can see that the model is detecting and protective helmets as

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well as jackets.

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So.

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Our results will be saved in runs the.

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And train on our interpretation too.

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And this is our output demo video.

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Okay.

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So.

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As this completed.

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Okay, so our results are saved in runs.

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Detect predict.

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True.

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So as our model is file is a bit large.

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So I think it will not be able to display here.

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So it's better to download this demo video from here and see what results do actually we get.

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Okay.

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So it might take some time to download.

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So I will pause, pause this video as it downloads.

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I will be back and then we will check what the output do we get.

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Was able to download the demo video and let me play showing you the output.

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So our model is able to detect the jacket's protective jacket, the protective helmet.

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You can see that the both person protected helmet and the jacket is rejected, although the model is

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not detecting the gloves, but the model is able to detect the jacket as well as the protective helmet.

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So let's test our model on some other demo videos and see what results do we get over there.

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So here we will download the demo video to and see what results we get on that test demo video.

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So let me test this.

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Our model on this demo video to.

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Actually, the name of the video is Demo three.

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So let's run this.

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So it might take two seconds to execute, but we can see that the model is detecting two protective

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helmets, one jacket.

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Okay?

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Two protective helmets, one jacket, two protective helmets, one jacket.

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It might take some few minutes to run.

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And then we will download the output and then we will see what results we get.

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Our output will be saved in the prediction three And this is the name of our output video demo 3.4.

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Okay.

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So I will just download from here.

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So it might take some time to download as the download complete, I will be back with the output video.

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Well, guys, I was able to download the output demo video and let me play the output demo video so

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you can see that the model is detecting protective helmet jacket.

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And here also the model is detecting the jacket, the protective helmet.

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The persons are not wearing the gloves, so the model is not detecting it.

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So let's test our model on the demo video three and see what results do we get over there.

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Okay.

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So let's test our model on the demo three.

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So I'm actually not displaying the results over here because these file sizes are large.

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So if not, we will not be able to show this output.

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Demo videos over here.

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I have already tested it, so not wasting time to show it, trying to show the results over here.

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So downloading the demo video three from my drive directly into the Google CoLab notebook.

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So it might take some time and then I will run the model on the demo video three and see what actual

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results do we get?

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So it might take some time.

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So.

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Okay, So motor is detecting two protective helmets, one jacket, two protective helmets, one jacket,

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two protective helmets, one jacket.

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Fine.

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That's good.

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It's the model's jacket into protective helmets.

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One jacket.

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Okay, So it's total 337 frames we have.

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And it's currently we have process.

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The model has processed 177 frames.

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Okay.

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So 206 frames to 20, 24, 45 to 50 6 to 62, 62 to 70 3 to 95 and to 99.

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And it's around over.

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So let me download this output demo video and show you what results we get.

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The output is saved in prediction for demo for.

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Let me download this output demo video and then we're back with the results.

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So guys, these are the results from my output demo video three So we can see that the model is able

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to detect the protective handmade jackets as well.

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So this person is not wearing the jacket, so the model is not detecting the jacket while these two

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person are wearing the protective helmet.

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So the model is able to detect the protective helmet and this person jacket.

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Now, let's test our model on the live webcam.

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Although I'm not wearing a protective personal protective equipment, but I want to see if I wear a

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mask.

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The model is able to detect it or not while on the videos and images.

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Our results are very good.

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So let's test our model on the live webcam in the next part of the video.

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Thanks for watching.