YOLO V1 - YOU ONLY LOOK ONCE || YOLO OBJECT DETECTION SERIES

Hi Guys, I am starting a new series about YOLO object detection model family. This is not an overview series, we will dig deeper into every detail of these yolo object detectors. Everyone uses YOLO models, they are the state of the art models for object detection. Hope you learn something from these videos.

YOLO Object Detection Series:

PDF links:

Timestamps:
00:00 Introduction
00:32 Object Detection background
02:10 YOLO - Intro & Steps
03:20 Responsibility of Grid Cells
04:20 Target calculations - Label Encoding
10:33 Understanding the Prediction Vector
13:35 Parsing the Model outputs
16:56 Model Architecture
19:27 Understand the Training Process
22:05 Breaking down the Loss Function
29:45 Fast-YOLO
30:35 Performance of YOLO
31:50 Generalization Capability
32:50 Limitations of YOLO
35:10 Conclusion

#yolo #yolov4 #yolov5 #yolov3 #yolov2 #yolov7 #yolov6

[MLForNerds]

Watch my latest in-detailed video on YOLO-V2 object detector.

[TimidMeercat]

After viewing multiple videos on YOLO workings, I found your video very detailed and helpful. Thanks!

[madhavpr]

Hands down the BEST explanation of the Yolo family found online. Great job brother!! Keep up the great work.

[shubham]

I am so surprised that, you are doing such a phenomenal job, (trust me: almost no YouTube channel does such a deep dive into theoretical understanding video!), but you do not have so many subscribers! I will definitely spread about this excellent channel.

[txtgdlp]

Bro ! I stuck to understand Yolo until I found your video. This deserves more than 15k views. now I know at least how Yolo working

[kvnptl]

🌟A very in-depth analysis of the paper. I would say this is one of the best easy to understand explanations of YOLOv1. Keep up the good work

[zaidazhari]

Thank you very much sir, i've been watching few videos regarding YOLO v1, but had difficulty grasping the loss function. But your video has helped a lot in understanding it 👍👍👍

[ParbatSingh-slko]

Loved the simplicity of explaining, and the presentation was also very minimal and apt. You really deserve more subs and views
🙌❤

[ahsentahir]

Great! I have not seen such indepth explanation anywhere. God bless you!

[consumeentertainment]

You rock!!! It was very detailed. Clearly, you have out a lot of work into this. Thank you so much🙏🙏🙏🙏🙏🙏

[aryangaur]

You are really awesome. My all concepts cleared.

[aishwaryamahajan]

Awesome Content, please can you also create videos on RCNN, SPPNet, Fast RCNN, SSD and FPN, It would vey grateful, if possible. Very well explained. Waiting for more on videos🙂

[giriprasad]

Great work in the image, class probability map says that cell occupies max area than we are giving that class and building targets we are just giving zeros to the cells which contains of center of object

[mdminhazurrahman]

your videos are gems bro!! I have not got such a clear explanation on yolo anywhere. please make a video on yolov5 as well. thank you!!!

[oqjuvpj]

Hi! thank you for your wonderfull explanation! Unfortunately in the original paper there are many unclear moments. Your video helped me a lot. But i still have some questions.
1) "Grid cell is "responsible" if the center of bbox falls into it." In training data we have annotated bboxes. But in test data there are no annotated bboxes and therefore centers. So which grid cell will be "responsible" in that case?
2) if c < threshold, then we simply nullify all the values in the vector or we should train the model to nullify the vector on its own?
3) if only 2 grid cells (in your case) predict the coordinates of bboxes, what is the use of the other 47 grid cells (are the useless at all or not?)
4) How one small grid cell (64x64) predicts a box for an object that is a way bigger than this cell (450x450)?
5) Why you are telling that there are only 2 object cells, if the woman overlap at least 6 cells? Maybe you mean only 2 "responsible" cells?

[sagaradoshi]

Hello, Great explanation on the content. Not seen such detailed content on YOLO. I have some question looking forward for your support.
1. Each cell can have two bounding box, but how is that the size of bounding box for each grid cell be different. For example in grid cell1 one bounding box could be rectangle and other as square. Or both are rectangles with different dimensions. So how is this possible?
2. Each bounding box provides x, y, w, h relative to grid cell starting co-ordinate and original/ground truth width and height bounding box. Correct? What I didn't further understand is how each cell calculates it C score value per bounding box and how it calculated probabilities value?
3. Then later you mentioned that out of two bounding box any one is considered for each cell based on confidence score of that bounding box * class probability right?
4. When you are calculating the final loss.
a. For cell with object, we took one of two bounding box and its x, y, w, h and c value and compared with ground truth value . Right?
b. For cell with no object, we took C values from both bounding box and subtracted with 0 since ground truth confidence score is 0 for that cell. Right?
5. Do we use IOU to calculate C value per bounding box per grid cell? If yes, how is it possible to calculate C value per grid as IOU depends on original size of bounding box which may spread across cells. Isn't?
5. To get this ground truth value for each cell (x, y, w, h, c, p1....p20) do we do manual annotation for all the images in dataset if its custom dataset?

Looking forward for your support

[ioljfin]

You are a god man ! Thanks for such clear and deep explanations of Yolo.

[daminirijhwani]

This is amazing could you do a transformer series!

[poojaverma]

Great content, very informative wating for the next versions...🙂

[vishnum]

Great content.
Can you create a videos on latest YOLO models (7).
Waiting for more. Good Luck!