Hi ML For Nerds - This is best explanation of YOLO V3 from which I could easily understand the working of YOLO. Please do more videos on recent topics in computer vision like - transformers, CLIP etc.
@MLForNerds Жыл бұрын
Sure, thank you!
@AdnanMunirkhokhar5 ай бұрын
Why you have low number of subscribers, you deserve millions of subscribers. Best Explanation (y)
@AndreiChegurovRobotics3 ай бұрын
Best YoloV3 video!
@giabao2602Ай бұрын
thanks for helping us a lot in learning, truly appreciate your work
@MuhammadRizwan-z2k5f5 ай бұрын
I had an presentation tomorrow of this topic, This video was a life saver bro keep up the good work, Would be more great if u had provided the link to slides and reference material but still thanks
@MLForNerds5 ай бұрын
Glad it helped. The slides are already in my github.
@kvnptl44005 ай бұрын
🌟Came from the YOLO series; one of the best KZbin videos with an easy-to-understand explanation of YOLOv3. Keep up the good work. If possible, make video on Vision Transformer (ViT) and then DETR. 🙏
@MLForNerds5 ай бұрын
Thanks, will do!
@emoji465224 күн бұрын
Thanks
@kvzui9943 ай бұрын
IM EATING THIS UP THANK YOU
@FuhangMao Жыл бұрын
Nice!!! Looking forward to other yolo versions!
@pavanKUMAR-tt4jt Жыл бұрын
Amazing explanation sir, this video helps me alot thank you
@MLForNerds Жыл бұрын
Thank you Pavan😊
@albertnagapetyan94827 ай бұрын
Nice!
@saisingireddy23593 ай бұрын
underrated af
@MJBZG2 ай бұрын
i still want to know how to code this stuff up? theory can be found everywhere but how to dive into doing it ourselves?
@haaanhson Жыл бұрын
Hi ML For Nerds, thank you for your videos ! I have 1 question that, with YOLOv3 we have 3 different shape of anchor boxes, but object have only shape, this mean we must be choose 1 shape, but as I see in the loss function, we calculate loss for all box, with this loss how to reduce the loss because 3 shape is different, how to get true bounding box ?
@MLForNerds Жыл бұрын
Hi, even though 3 possible scales of anchor boxes are generated, each ground truth box is matched with only one of them based on IOU. So loss is calculated only for the matched boxes.
@haaanhson Жыл бұрын
@@MLForNerds thank for your respond, can you explain more detail, because as see on loss function formular, I do not see loss is calculated only for the box which have highest score of IOU, I only see loss = sum(loss all box - ground truth box)
@user-pc5zl7ji1e Жыл бұрын
hi thank you very much its a very good explanation . i have a question in prediction across scales slide you explain that yolov3 architecture contain 106 layers but it based on darknet53 so i think it consist of 53 layers what the true architecture ?
@MLForNerds Жыл бұрын
Yes, absolutely. But Darknet53 is only backbone and it has additional 53 layers on top of the backbone to make it 106 layers.
@user-pc5zl7ji1e Жыл бұрын
@@MLForNerds what is the kind off the other layers fully connected layer or else
@safiullah35311 ай бұрын
please make one another video about yolov5 sir
@MLForNerds11 ай бұрын
Sure, I will start yolov5 this weekend.
@neeru1196 Жыл бұрын
Hi! Thank you so much for your videos. It would be really intuitive and helpful if you could show what different operations/transformations look like in the Yolo process. For example, when the image is down-sampled using convolution, and then the features from previous iteration is re-introduced and up-sampled, what does the physical image look like?
@slobodanblazeski09 ай бұрын
It's not an RGB image anymore that we could just see, instead its the feature map of the filters, the deeper in the hierarchy you go the more abstract features become. Google Visualizing the Feature Maps in Convolutional Neural Networks for examples.