YOLOv3 in the CLOUD : Install and Train Custom Object Detector (FREE GPU)

[Music] welcome to today's video everyone there's a lot in store for you today in today's video I'm gonna be covering Yolo version 3 how to install and run it in the cloud taking advantage of free GPU that anyone can use and I'm also going to be showing you how to train your very own custom object detector in the cloud using that same free GPU so really anybody can follow this tutorial and will be able by the end of it to have their very own custom object detector that will be able to detect all sorts of objects whatever you choose whatever you can imagine you'll be able to do it let's do it alrighty so first things first you're gonna notice that we are running our notebook on Google collab so we're gonna be using Google collaboratory for this tutorial so you will need a email which is connected to Google Drive so we have access to Google Drive and that's where we can create collab notebooks so if you don't have one of those go ahead and create one takes like 2 to 5 minutes because Google collab offers everybody free GPU acceleration on their cloud VM so that they can run programs for up to 12 hours at a time using GPU and it's totally free so that's what we're gonna be using for this video so it's exciting and I'll show you guys how to get this notebook up and running for yourself so you're going to go ahead to the descriptions comment the link to this notebook itself and then you can actually just go ahead and I'll show you there's two ways this is the first you go ahead and paste it into a new browser or your browser using new browser for me so that doesn't automatically log me in and it should look like this you're gonna go to open and playground and then there should be an option here just to copy to drive if you click that it will open up this Google Notebook for yourself that you can edit and actually run commands because I have it in view only mode so that none of you guys can change it for so yeah that's one way easily just click copy to drive and it'll pop up in your own Google Club otherwise you can just search in Google Google Club Google Club and it will actually open up a like and welcome notebook for you and you can go and file new Python 3 notebook or upload notebook and go to my github which will also be in the description below Yolo v3 cloud tutorial and you can just go and download as a zip and then unzip it and upload this tutorial notebook to collab just like that file upload notebook and it'll come up as well so yeah there's two ways to do it I recommend just doing the copy to drive way much easier and it'll open it up with all the images and stuff for you because the images are a little wonky down here so now I will exit that so now back to the notebook how do we actually get GPU enabled right off the bat so I have pictures here for you but I'm just gonna show you you just go to edit and then you go to notebook settings and this will be set to none you're just gonna go from none to GPU click GPU and hit save and now you are this little cloud VM that spins up for you in the notebook is now GPU accelerated and you guys can run programs using GPU so if you've had headaches in the past with your computer not being fast enough or not having a GPU that problem is solved hahaha so now that GPU is enabled in our notebook we can go ahead with the tutorial so we're gonna be cloning and building darknet from Aleksey abies famous repository and we'll be building it using make but that all comes pre-installed on here the it's like a Linux VM so we can run the make commands don't have to worry about downloading anything else so first things first you can run each shell by clicking on the play button one at a time and going down you can also just go up here really click and click one button run all and that will do all this base work for you cloning darknet into our VM so we go to the file system we didn't have darknet here before and now darknet is here so it's cloned it into our VM we can see we have all the files so that's perfect this next cell what it does is it is actually in line editing this make file within the darknet folder and it is turning open CV equals zero to one GPU to one and couldn't enter one that is enabling us to build darknet with open CV and GPU enabled so in the past you might not have been able to put those variables but you can everything comes pre-installed on your VM like open CV and see udn and then the next cell is just verifying that yes cuda is on your cloud vm and if you didn't know each notebook spins up a cloud vm associated with it that you're connected to so that when i talk about cloud vm it's just the notebook itself but yeah so you can see that we have version 10 from nvidia that's perfect and then this big command which takes us a couple minutes to run or a minute or so is actually building darknet by running the make command it's building darknet and all the dependencies from the binary files and it will allow it to be run because of those flags we changed to run with GPU enabled so yeah it's if you may not been able to do that in the for before or if you're on Windows it still works because we're in a cloud VM so yeah it's super easy just one line make and it will build it for us so we're gonna scroll down it's built and now we're getting the pre-trained Yolo version three weights from the official site this will allow us to do detections on 80 classes right out of the bat it comes out of the box with trained model on 80 classes so we're gonna take advantage of that to show you just that we've installed and built darknet properly Before we jump into training our own custom detector so it's grabbing the weights files and saving them to the darknet repo what we jumped ahead a bit there oh you can see it's working huh but yeah so we go back up here we've got the weights saved it here we can see it you'll love you three weights perfect we've gotten it and then the next cell is a couple helper functions for us to show images upload files and download files to our cloud VM for a notebook so now we're actually running the detections if you want to see the classes that out of the box Yolo weights can detect what classes they can detect objects click on this and it'll show you little buttons and show you all the different classes so now comes the time to run our darknet detect this is the command darknet detect to your config file the one that comes preach pre-trained we have it the weights and the image and then m show predictions JPEG allows us to pop up the image because the cloud VM doesn't dynamically do it right after you detect like it might on your local machine doesn't have that capability so this helper function will just show us the predictions on the image so we can see that it ran the first example on data slash person this comes with repository so you will have this already so this will work for you it's loading in all the way it's in all the layers and then it's you see this warning that it cannot open the image that's what I was talking about so we run the M show with predictions JPEG every time you run darknet it will save the image with the detection Zoll ate on it to predictions JPEG so if you're ever looking for that it's just in the darknet folder and it is predictions JPEG where is it right there perfect so it's there and we see that we ran the detection on person JPEG it has detected a person a dog and a horse within the image and if we scroll back up at the bottom of the output it will show you the detections and the confidence level so the dogs 99% person 100% and the horse is a hundred cent and you can see that it predicted this image in 21 milliseconds 21 milliseconds that's super fast super super fast a split less than a second like so much less than a second if that was not GPU enabled that would be about like seven or eight seconds per detection but we were able to run it with our GP you are free GPU in 21 milliseconds which is absurdly fast that's real-time super super fast so that's the power of GPU enabled darknet and then we're again this is just a second detection and it's gonna predict it right away at the bottom it's loading it in this time we doing the dog image and this is another classic image we can see a detected bicycle 99% confidence dog 100% and track 94 and then it's showing the image because we ran the show function and you can see the detection perfectly so run all will go all the way down to this tenth cell this cell right here is cert equals false just runs it so that when you run all it will cause an error here so that it no longer continues to run because the next cells and all the way down are dependent on certain files that you may not have yet so that's why it runs here but if you just at the beginning go run all it will go all the way to this point hopefully well it will I'm 100% certain because it works on all the VMS perfectly with accuracy and you should see all of this up to this point just by doing one-click runtime run all so that's pretty sweet so if everything's working for you up to this point I would go ahead and probably click this link right here pop it open oh wow what's that jeez I would definitely hit this button right here too bad I already have but I would definitely hit subscribe and like this video please if you're enjoying this so far it would mean a lot and you'll be up to date with all the latest Yolo v3 content and all the new coming so hit that subscribe button smash a like on this video I'd really appreciate it thanks guys so back to the actual tutorial we now can upload local or Google Drive files to use so if you want to run detection more than just a couple images there are within the repo itself you can do this so you can upload it find one of a cool class of one of the 80 classes like there's toothbrush kites skateboards like this bunch of them is a tea that you want to detect for and you can go ahead and actually choose files to upload I show a couple examples here yep running this upload command just lets you pick an image from your local computer so I can go ahead and do this let me go to downloads and I will take this photo I will link all I will have all these photos that I use within the tutorial in my github repo so don't worry they're in there you just go to my repo and they're in the images folder so Street saves streets and now I'm actually gonna run the detection on the street so you hit this and it's gonna go ahead and run the detection on the photo that I just uploaded to the to the notebook you can see Street JPEG right there so you can see this one it's a busy intersection of a street and you can kind of get a grasp of the crazy classes that Yolo can predict straight out of the box like it's getting a cell phone out of somebody's hand that's pretty far away like how like that's pretty sweet it's getting the cars on the road bus behind us traffic lights themselves handbags and of course a bunch of the people while all the people walking across the street so out of the box that pre train weights are crazy good and you can use them for a lot of different applications you can also mount files in from your Google Drive and I'm gonna recommend this as a way that you move your files around so everything gets saved so if you go to my drive I have an images folder with two images I'm going to be using the plain one right now so you're gonna go ahead and run this command and it will mount your Google Drive in you're gonna click this link you know copy the confirmation code that you get from once you click on the link and you're gonna go ahead and paste it back in here and that's gonna go ahead and connect and mount your Google Drive so everything you have your Google Drive will now be accessible within the notebook so we're perfect that we can see that it worked and now if I just list what's in my drive I should be able to see what's there so it can see it has calab notebooks images in Yolo I go back to my drive and I have calab notebooks images and Yolo v3 and you'll love e3 is where I'm going to hold a lot of my configuration files that we'll get to later for training my custom object detector so now I have that I'm going to now point it to that plane image that I showed you and we're gonna see if that works so it's now again look at this plane JPEG image and hopefully run the detection zhan it so it's loading in the model and airplane 100% that's pretty cool perfect it got the airplane detected so we've uploaded files and now let's download files so how can we get these files back to our local machine and save them because when the runtime ends and you close the the cloud VM anything saved within here will disappear and you won't be able to access it again so any files or images that you want saved to your local machine or Google Drive you will have to download them so just hitting this will download your local machine using our helper function it just will go ahead and download predictions JPEG perfect I'm not gonna open it I trust it and then this command copy will take predictions JPEG and save it to my Google Drive under the images folder and call it detection one so this is a quick way to just start firing images into your Google Drive when you run detection so I'm gonna go ahead and run that and we're gonna see that it's gonna copy over the image and it should pop up now in my Google Drive if I reload this give it a second another reload and there it is it's called detection one what I renamed it and we have the detection of the aeroplane of the airplane so it's pretty cool you can see how you can easily move files to your Google Drive download them upload them and everything and now comes the moment of truth we're going to start training our custom yolo' version 3 object in the cloud on the GPU for free I know it's crazy who can believe it and you guys are killing it so far if you gotten to this point it's about to get a little trickier but we're gonna get through it so in order to run your Yolo version three detector you need a custom data set a custom config file object data and object that names files and a train dot txt with a test txt as optional I'm not gonna show that here but you can do it so there's two ways to gather custom data sets I have two previous videos one where I show you guys how to use Google's open images data set which has millions of images that come protections on them so that you can then just download them and their classes and I've shown that in a previous video you can link to the video right here and explore the data set open images by looking at this link and then I've there's a toolkit which helps you download all the images and convert their annotations to yo lo form and I've done that might get robbed repo here so I really recommend this way this is the better approach superfast and open images has 600 classes that you can choose from and create a custom detector for so go ahead and I show you guys the commands that I've used what the file should look like in order to do it so you can just follow these steps and easily from this repository create custom data set so go crazy look in the open images data set and find weird classes that you can create your detector for for mine I'm just doing kind of a safari animals so I've got elephant giraffe hippo tiger and zebra images and I'm gonna be creating my custom detector to detect those animals so that's the first way the second way I also have a video on this is the link if you want to go watch it you can mass download images from google images and then use a to an annotation tool to go and actually manually label all the images as you can see that's a longer process and a little bit more tiresome so I don't recommend that unless you have no other way of getting the images you want for your custom detector and that's the only way to go it is a viable option it just takes some time to create the manual labels but yeah yeah you have to do it so now that you've got your data set everything's under control you're gonna have to move your data set into your cloud VM so that you can access it and train on it so this is recommended because it greatly reduces the time to fetch files and locate files if they're already in your VM so quick commands so once you've got your data set you should have a folder that looks like this JPEGs and txt files one for each so if I go into my folder of the toolkit you can see I use the toolkit originally my my file name with all my photos was called this you're gonna want to go ahead and rename it to Abdi leap any there's a label folder here after you have all the delay Buhl's delete that we don't need it but yeah so we can see it looks properly looks proper I've gone ahead and renamed it or the traine custom detections training it and then you're going to want to go ahead and right-click and go sendto and do a compressed folder is it folder so you're gonna want to zip it so that it isn't we're not like downloading thousands of images because this data set I created has thousand five hundred images we don't want to transfer that it would take forever so you're gonna zip it and then you're gonna go to your Google Drive and I recommend creating a Yolo v3 folder for everything and you can see that I just you just drag and drop it into here it's downloaded so now I have objet in my Google Drive and I can access it inside of our notebook that's awesome so now I can just list my drive and make sure it's there that I can see it I can see objet what this will do is it's going to copy the zip file into our cloud VM at our root directory and then the next command is going to go and unzip it in to the proper data folder where we need all of the data for our custom detector that we want to train on so perfect and now I'm gonna go ahead and unzip it and you can see it's now inflating all the images and text files inside our cloud VM at a much faster pace than if you just transfer it over all the files without zipping it so yes zip it it will save you tons of time do it and call your files the same names as mine so you can just run these same commands so now we need to configure our other files are configuring files for our custom detector so you probably have seen this in my previous video how to train a custom object detector that was on your local machine the steps for the custom these configuration files are the exact same so I recommend going to check out that video starting it at like the 5 minute mark I think that link does and it'll show you how to prep all these files but if you want to see them how I did it again I have them in my repository my custom config and my object data's and names so you can look at it but yeah it's gonna you can also so what this does is this command here this next cell it will copy I'm just gonna change the name here so I don't accidentally overwrite what I already have in my Google Drive so it's gonna copy the normal one that works for the pre-training model config file from the config folder in your darkness in the dark nut and it's gonna copy it to your Google Drive and that way you can edit it if you run this command it will just download it to your local machine and you can edit it so you're gonna have to edit this config file in a text editor or Co editor I'll show you actually how to do it here so I will download it to my Google Drive perfect so it's gonna be hopefully in here if I reload in a second yep this is the one I changed name to two because I didn't want to this is the one I'll be use for my training I've already edited it but what you're gonna do is click double click on your file open with and go to text editor this is super easy and you can edit in Google Drive so I recommend doing this and what you're going to go ahead and do is start off by commenting these two lines and uncommenting these two lines so this will set it up for training batches 64 and subdivisions 16 that should work and then you're going to go ahead and change your max batches to 2000 times however many classes you're training for I'm training for five Safari animals so I'm gonna change this to 10,000 and then you change your steps so you change your steps to 80% of your max batches so this is 8,000 for me 8% of 10,000 and then this one is not 90% of your max batches so that'll be 90 for me and then the other changes you make are you just control F and look for Yolo and in each of these Yolo layers the square brackets is a little error you're gonna change it to your number of classes so I have five classes the five Safari animals and you're also going to change the filters in the convolutional layer above it and this you're going to change to it's going to be the number of classes you're training for plus five times three I'll pop that up on screen to show you guys so for me that's 5 plus 5 times 3 is 30 and you're just gonna go and do this for each of your Yolo layers and each of the convolutional layers before them so five and thirty five and thirty and then one more there's three of each five and thirty and then you're just going to go ahead and save it to your drive one thing to note if you run into errors later down the line I recommend that you try with random equals one what that does is as you're training it will change the file cite the image files in size scale them up and down so that your detector is used to seeing different size images and whatnot but if you run into any errors for some reason on your VM that says like out of memory or its if you want faster detections faster training time I mean and I set all those to zero but leave them as one to start okay so save and now we can go back so all of these are explained in here you can go and I explain the random equals one if you want a faster training but now what I'm gonna do is copy my custom configu back to here so I'm just gonna go ahead and copy it back in and now come so now I should have if I go into my dark nut config and scroll down to the bottom I have that custom configured and it's now back here but in the proper version that I need to run my detection to run my training I mean so now comes time to create your own custom object names in object object data files so I explain these in my previous video as well but I'm just gonna quickly show you guys again so you in a text editor or code editor there on your local machine or just straight from Google Drive going to new and going to more you can create text files so you're gonna want to have your object names and this is going to be each of your classes one per line that matches exactly the classes dot txt file this is too far up there that you did for the toolkit if you use the toolkit it has to match exactly the same so that's all your class is and then you're also going to create your object data folder which is kind of just like a legend of where everything is for the training model so that when it trains it knows where all your file folders are so what you're gonna do is just make it look like this first lines classes in the number of classes that's all you're gonna change keep these the same even though text txt we're not going to be using it that points to where we just saved object names or where we're going to save out with their names and then for backup so backup is the folder that throughout the training process it will save weights to that will allow you to run the detections and see how it's doing so the weights folders are yet what loads in and is allows your custom detector to do its custom detection so it's the most important part and what we're doing here is we're actually saving it into our Google Drive so we're saving it to our Google Drive in our Yolo v3 into this backup folder so your shouldn't come in here and go new folder create a backup folder and this will work for you one thing I just noticed that I forgot to mention that way up here that we're doing is when we first create the link between our Google Drive right here we're creating a symbolic link so the original path when you mount your Google Drive in is this content G Drive my Drive and it has a space in it my Drive and I found that a lot of commands that was causing me errors having a space in my folder structure so what we're doing is for king of symbolic link so that every time we're trying to call that we can just equal it to my drive and it's much easier doesn't have a space so doesn't really screw up anything so that's what this file is it's just creating that link mapping this file extension to that file extension and it works so Fred mention that just wanted to mention that so that's perfect so yep we're creating that and we're pointing the backup to save our waits files to our Google Drive and I'll explain why later so let's download my object names and object data that I've created and that are in my Google Drive so let's copy them in perfect they're gonna go and finally we're gonna create and generate our trained txt file this is a file that just shows all of your the past relative to where your training from to all of your images I also have that and my github repo right here so it's in it's in here as well so generate train just download it put it to your Google Drive and then you can run this command to copy it in or upload it by uncommenting that line right there so I'm gonna copy it in generate train and then by running that command it will generate the Train txt file for you with all the fat over a thousand images that I have so if you were doing that manually that would take forever I've created a script that does it for you so I'm just now gonna run generate train and it will generate that file automatically for and if I just now LS to make sure it's there we can see train dot txt which wasn't there before and if I want to open it it would look like this it has just one per line and it points to each image not the text files and so open that by going and finding it in data train dot txt double-click and it will open that up on the right side and make sure that yours looks the same as this so the final step right before we can now detect our training is to download a pre trained convolutional layer weights so what this does is it kind of scopes out the model architecture for you automatically so we're gonna go ahead and grab that and it so it's not your that your model isn't starting from like net zero scratch it's building off of these weights that are already like already optimal for each of the layers it's not like the weights to detect all the classes it's just the layers themselves we'll learn these weights over time so we're just throwing it in so that it doesn't have to learn those weights it already has them so it's kind of confusing I know but it just makes it so it's faster you converge faster in your model can start working properly and accurately a lot faster than if you just did it with out this so use it use it use it run this command get the darknet 53 comm 74 file in and now we can train our custom detector so now we are ready to do our custom detection z' which is sweet so we can now i show some tips and tricks for running it so one thing to note is that google collab has an idle time of between thirty two it's 90 minutes that if you're not on like doing anything inactive it will kick you off its VMs because it doesn't want to waste GPU like having it stored waiting aside for you when you're not using it which is kind of fair but this these commands right here will allow you so that it generates an auto click every ten minutes in your console so that it thinks you're still active even though you're not so it's kind of a hack to not get you kicked off of Google Club sorry Google don't hurt me for this but yeah it works because this training will take several hours depending on how many iterations you chose for max batches and your config or how many classes you're trying to detect for it can take a long time to run so what you're going to want to do is copy this just go to copy selection and then hit control shift I and that'll open up the console inspector and what you're gonna go ahead and do is just copy paste it in here and click enter and that will stop it from automatically kicking you off and saying that you're idle so that you can run this and kind of walk away from your computer go to dinner go with friends come back and have it running I also recommend going to your settings and going to your power and sleep settings let me go to I know it's from display so you go to display settings and then go to power and sleep so just in your settings and I always check these when I'm running you although when plugged in don't turn off and when plugged in I never go to sleep so that way when I'm plugged in my computer and letting it run on here it won't turn off and stop the the cloud VM from running so that's also maybe a good practice to do but now comes the time where it's ready to run you just follow this command if you've done everything properly and you run this don't show flag because it'll keep trying to pop up a chart showing you how your trainings doing but that will actually cause it to break because I said on the cloud VM you can't pop up images dynamically so we're just gonna go ahead and click train and hopefully fingers crossed everything should work for you here and you should start training your custom detector it's that easy hopefully you haven't had too much troubles up to this point and you can see that it's resizing images that's what that random flag does so if it's trying to allocate additional work space for you and it's failing here because maybe you don't have as much storage on the vehicle at VM that you're using then I do just go back to that config file and set random equals zero so this won't happen but you can see it should be working if you look here this is the iteration we're on we're gonna iteration three and this is your loss value my loss is over two thousand right now but you want that'll get down to hopefully under two when that loss is under two that's when your models fairly accurate and detections should start working properly but you can see it's doing an iteration every five seconds or so and that will change based on the image sizes that it's detecting for so it will take a few hours to run this but with that command you can just go for go work for the day and stuff like that and when this isn't running you can actually just show this image chart PNG and what that does is chart PNG will show you a nice curve of your loss versus iterations over time so that you can see how your model is doing I can't run that right now because it's training you can't run two cells at once I don't think and then here's a trick that while it's running it will save this Yolo custom last weights to our my drive in our Google Drive backup I've run this before so I have some other weights here so it will save a new file here with your up-to-date weights every hundred iterations through which is perfect because say something happens and you your Google club goes idle or it kicks you off after twelve hours for some reason you don't want to lose those weights because if you had it in here your backup you would lose that if it crashed you would lose the weights and have to start from scratch again and that is brutal but because we're saving it in our Google Drive it will continuously keep our weights in here and so that if it crashes we have the weight we have the weights thank God you don't know how annoying that is when you run this and the weights get lost for out after hours of training and then we can actually load the weights back in and do the exact same detector train command from the last weights and it will pick up training exactly where it cut off so I can actually go and end this let's just see what iteration we're on it's really far down okay let me scroll down on this you can see that it's working it's powering through so if I go to I'll interrupt it and show you that this works so if something reason got cut off now clear the output for this one and I'm now gonna run this command the same command but now pointing instead of to the darknet pre-trained weights to the weights that i've started building off of and we'll just run that command and it should it's loading everything in properly and we'll see that it'll pick up training hopefully we're late off and this for me will be because I only saved three hundred iterations and I didn't get two hundred iterations this should be for me somewhere way too way different so yeah I trained this earlier up to twelve hundred iterations to show you guys you should let it keep going forever and my loss is under one so you can see that it goes down pretty quickly so after twelve hundred iterations it's down to under one after about three hundred iterations hopefully you're down to under five under five for your loss value but my twelve hundred is loaded in from where it stopped when I cut it off earlier at a previous time not on this demo so it's loaded that in perfectly so I can now stop it again and showed that for some reason you cuts off you come back your computer crashed go to your Google Drive backup and you have custom lost weights and it will also save a weights folder every thousand as a new file so this one's called a thousand that's one would be two thousand three thousand as you go so that works well so we can now run our custom object detector so let it run for a while come back let it train this is perfect minds been training for a while I trained it over a thousand iterations you would want to keep training it and longer to get see it keep you back lost curve going down and down as far as you can but for me this works perfectly and now I can run my custom config so right now our config file is pointed to have 64 batch size and subdivision 16 to run your custom detector you're going to want to set those back to 1 and we can do that in line with this quick command so I'm just going to run it and we can now run our detection using this detector test command so you first parameter point it object data your custom config and then now you point it to your weights so my drive my custom last weights and the image you want to point it up to I'm pointing it at a Safari image and then this threshold parameter shows that anything under a 30 percent confident I'm not going to show you can put that in if you want you don't have to and then it's gonna show it so let's run that and it's using it off my custom lost weights which you just saw was twelve hundred iterations so it might not be perfect but it's running our custom detector and it will open it in a sec boom there we go so this image I didn't it doesn't show everything perfectly because it doesn't have tiger and hippopotamus and stuff but you can see that after a thousand iterations and after that was twelve hundred to be exact it's pretty decent already knowing that that's a zebra and that's a giraffe obviously it's not perfect it didn't catch the zebra or the zebra which would have been really hard but I was gonna my detector was gonna train on ten thousand iterations and that was only twelve hundred so like just over a tenth of the time that I was gonna let it train for if I was trying to optimize this just for the purpose of this tutorial I cut it off early so I could get this video to you guys but yeah so if you're running for something that you want perfect detection zhh keep running it all the way to that max patches and then I can also show you that chart PNG just show you a good representation so you're just gonna pop up in this chart PNG and if so right now it's not showing anything because I hadn't let it save again after loading in my new detector wiped it but you would see after your custom training and just leaving it in one run that you'll have a blue graph drop all the way down here because you can see that mine just started to plot again right here because that's where I just picked up training because every time you stop training it wipes the stuff from before and it starts new the chart so my previous detector went down steep steep steep to here and I just started to Train from this point and you would have seen it go down even more so you can see that one blue dot but if when you do this you should see a lot like hundreds of dots going down showing that your loss is going down over time so that's pretty much it guys now you've been able to hopefully be able to train your custom detector and I did a safari one and hopefully you guys can do your own show me some cool stuff I'd love to comment down below what you're detecting on like what your classes are I'd love to see kind of cool custom detection that you guys are trying to detect so comment down below what you're detecting on and also if you could please smash that subscribe and like button so that you're up to date with my newest videos and it helps the channel grow so I appreciate it thanks guys see you next time [Music]

Channel: The AI Guy

Views: 95,379

Rating: 4.9634771 out of 5

Keywords: yolov3, yolov3 cloud, darknet, object detection, yolo, custom object detection, train yolo in cloud, cloud, object detection classifier, train yolov3, install yolo, google colab, yolov3 tutorial, how to train yolo, custom detector, yolo v3, yolov3 darknet, custom yolov3, darknet tutorial

Id: 10joRJt39Ns

Length: 41min 49sec (2509 seconds)

Published: Mon Jan 27 2020

Reddit Comments

[u/a_beautiful_soul_]

I've followed this tutorial. It helped me a lot. I've watched your other YOLO tutorials too. Thanks a lot for your great work! I've trained my own YOLO model recently but I don't know how to deploy it on a web-app. I've heard it is done using Flask but I don't know much about how to integrate it with front-end. Will you be doing a model deployment on web-app tutorial anytime soon?