In the last video we looked at some theory behind YOLO algorithm. In this video we will do real object detection using YOLO. Now we'll go over some history behind YOLO, the different versions - version - 1,2,3,4 As of January 2021, there are total five versions of YOLO. So we'll go over that whole history and then we will use pre-trained weights for doing object detection. So we are not taking any custom object image Dataset and then doing object detection in that instead we are using COCO Dataset using pre-trained weights and doing object detection for those COCO Dataset labels. In future we'll have a separate video on how you can train your own classifier or not the classifier but the object detector you know, and run YOLO on the custom Dataset. In 2016 the first version of YOLO was invented by Joseph Redman, Santosh Divvala, Ross Girshick and Ali Farhadi. I'm going to provide a link of this research paper in the video description below. You can just . it and you can find what they proposed in this particular research paper. Then in 2017, they came up with YOLO version 2 mainly Joseph and Ali Farhadi, where they improved the speed. You see that they have speeds which supports the rate of 67 frame per second . So this algorithm detects the objects at a very very fast rate . So if you have a video playing at certain of frame rate, it can detect the objects really fast and this could be useful in let's say autonomous cars. In 2018, a YOLO version 3 was created and again Joseph and Ali was the pioneer in this incremental improvement . So they kept on improving the original YOLO framework till version 3 and then at some point Joseph Redman stopped working on it because of some concern, some conflict that he had with his personal values and the use of this in military. In 2020 YOLO version 4 was created. Now the reason I have this in different color was because Joseph Redman was not part of it. Alexey Bochkovskiy created a fork of his GitHub repository and he created this separate branch of this version 4 algorithm. Nowadays there is YOLO version 5 as well but there is some controversy going on with it so I'm not going to cover that. We will be doing coding using YOLO version 4. You need to go to Google and search for YOLO v4 GitHub AlexeyAB and you will find a repository by AlexeyAB that has YOLO v4 core and this is the repo name is darknet it is forked from pjreddie . So this one is if you look at this repository, this is the original repository by Joe Redman and he stopped development of YOLO after version 3 . So then AlexeyAB forked his repository and he built this YOLO version 4. Now YOLO is not available as like you can't do like paper install YOLO something like that. You have to get clone this repository and follow certain steps in order to use it. Now once you are on this repository, you will find this particular section where it says 'how to compile this on Windows' If you are on linux there are instruction on doing this linux . So this was built originally for linux so building this on linux is straightforward. On Windows you have to follow certain steps, it's not very very straightforward . So first what you need to do is git clone this vc package and vcpkg is needed to compile the dark net project . So what I have done is here - open a PowerShell So when you open a PowerShell Windows PowerShell, click on this and you get this Windows command prompt this is like PowerShell is a new version of old Windows command prompt and here I made a temporary directory okay. So if I do cd tmp and see this is an empty directory which I made . So now I need to do git clone and you can copy and if you mouse right click it will copy that . So this will copy the vcpkg Microsoft project once it is copied see you're just doing copy paste . So you go to that directory you set some environment variable this is just setting some Windows environment variable and then you are compiling your bootstrapping that vcpkg will might take time. okay so after a few seconds this step is done. Now I'm just, see this will install the whole darknet version. I want to install only the one for x64 Windows So this will save you a lot of time if you try to install the whole version it will take hours and hours So here I think I did not copy the whole thing so okay . So what you need to do is this install darknet opencv CUDA cudnn okay So now again it will take some time for this tip to finish So it is installing the cudnn and all these dependencies. I forgot to mention one thing is before you do all these steps you need to perform these three steps where you need to install either visual studio 2017 or 2019 the community edition okay community edition is free . So that is Microsoft IDE and we need that for compilation process. You might be wondering why are we installing visual studio? Well it is needed for the compilation process . So install that then you install CUDA. CUDA is Nvidia's library for GPU and Machine Learning so install that make sure you enable VS Integration okay . So CUDA is not really machine learning it is GPU library you know it allows you to access GPU for faster computation But when you're installing that make sure you are doing VS Integration . So see when you do CUDA installation . So basically when you do CUDA installation, just go to custom step and make sure you install everything they will have express tab but don't use express tab go to custom and install everything including VS Integration. When I was following these steps I was facing horrible issues with CUDA because I had CUDA 11.0 or 11.1 and it was giving me a lot of trouble because it was not compatible with the rest of the software I have on my machine So I had to install CUDA 10.2 okay . So if you are facing any errors at any step let's say at this step when you're compiling darknet if you face the error make sure that you look at the error properly. If it is saying that the error is in this particular file path, open that file and try to see the error most likely it might be incompatible CUDA version . So try to resolve that. Again following these steps it might be easier or it might not be easier based on what kind of system software you have . So you have to have a patience, look at errors carefully, do Google search and try to install the things which are needed. This is still downloading looks like this is slow. Now that step is done without any errors so we are ready to move on to the next step which is go out of this directory . So you'll see that right now this c code temp directory . So if you see code temp this has only vcpkg . So see this is vcpkg Now you're coming out and you're doing git clone the darknet repository . So once you do git clone you will have that particular directory here . So if you do dir see you have now darknet repository here and then you do cd darknet and then now this command doesn't quite work well like we can try it . So see some authorization issue it is giving so we will be running a different command which I have given in the video description below. So this is the command that will run. Again it's available in the video description below . So check it out. So now this is building the dark net repository and it will take some time for it to build. It took me long time around 40 minutes for this to finish . So make sure you have enough patience. This process, the compilation of darknet is going to take long time. Now when I look at my tmp directory here in the darknet folder I will see a thing called darknet.exe Using this you can do object detection and also here there are a couple of files, for example - there will be this image yolov4.sh So if you open that here this has a command to detect the objects basically So if you look at data directory here data directory dog.jpg So see there is this image where there is dog, bicycle, car and so on and we want to do object detection on that so it draws the boundaries around this . So when you do this command here, just copy paste this command okay So this will run the detection and now see here it says the yolov4.weights couldn't find . So we missed one step which was to download YOLO weights from this particular folder . So let's go back to the directory here and if you do yolo. YOLO for or just do weights See use this link. When you use this link you will be able to download those weights . So when I do this see it is downloading this now . So I copied that file into darknet directory . So yolov4weights that you just downloaded copy it in this darknet directory and then run the same command again. See now it detected those objects you see that . So there's a truck, there is a bicycle. This these are like scores you know 0.96 percent this is matching with a dog, bicycle, truck. So you can see that when you have driverless car type of application this kind of object detection can be really useful. I have one more image where I'm trying to break my computer okay . So let's detect objects in this particular image . So the name of the image is laptop.jpg you can put any of your image . So here I will just say laptop.jpg See it is detecting some of the images right but for this hammer it is saying toothbrush. The reason is that the COCO Dataset probably doesn't have this hammer as a label or maybe or maybe it did not detect it fine . So let's look at our COCO Dataset labels . So in the previous video I talked about different Datasets such as COCO image net so watch that video if you don't know what COCO Dataset is but it's an image Dataset with all the annotations, pixel segmentation and so on. And this and the weights that we downloaded where train on that COCO Dataset and if you look at this website this has all the labels of COCO Dataset . So the link of this website is in the video description below. You can see that it has around, let's see around 91 labels and like 91 classes which it can detect . So whatever output you get will be one of these classes and this probably doesn't have a hammer So you know but it has toothbrush . So it is trying to do detect toothbrush . So any image you take which has any of these objects it will do the object detection all right. So this was on the COCO Dataset if you want to train YOLO on your own images, there are instructions here we are not going to go into those details in today's video. Maybe in future we'll cover that but just follow these instructions to train your own custom object detection model In the future video I want to cover a Recurrent Neural Network and that's the reason we don't want to spend too much time in these videos. So we will be starting a Recurrent Neural Network and NLP tutorials pretty soon. I hope you like this video. If you do please give it a thumbs up. Check video description it has all the necessary information and the links that you need. Thank You.
