YOLO Object Detection (TensorFlow tutorial)
You Only Look Once – this object detection algorithm is currently the state of the art, outperforming R-CNN and it's variants. I'll go into some different object detection algorithm improvements over the years, then dive into YOLO theory and a programmatic implementation using Tensorflow!
Code for this video:
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Yeeeee I waited for so long for yolo
The Magic V, do you want to have a tutorial on Google Speech API, i.e., convert your speech into text!
Watch this:
https://youtu.be/jc_-AIYvfKs
Bro you might not know this…but you’re pretty good at this Youtube thing lol. Thanks man you’re the best
The secret is use deeplearning to improve the video
Watch me man!
https://youtu.be/jc_-AIYvfKs
Thanks George lots of practice
teaching is the best way to learn
I literally just sat down to do an assignment on this. Siraj, your timing is impeccable
thanks!
@Siraj Raval, can you comment or make a video on how YOLO is trained? Are the two parts trained on different networks and then combined? Or are they all trained in one go? More info would be appreciated.
I just liked this comment to bring the total to 69 😀
Hfish21 please can you tell me how did u do all this work… Because its my project work.. It need it at any cost please
Hey my name is naazim I have made this video on detecting actions in basketball match with Yolo, tensorflow etc
Pls check it out if you are interested in this topic
https://youtu.be/0X6yTkXn-qQ
At 4:10, HOG does actually mean Gradient in the same way as backprop does. An image is just a discrete representation of a continuous 2D signal, the gradient of the continuous signal at a point can be approximated from the discrete representation by taking the finite difference between neighbouring pixels.
yeah I was surprised that Siraj didn’t know that this was identical to a gradient.
I think he meant the gradients don’t have the same function as they do in backprop, i.e. representing an error value
So pretty much like a vector in physics.
Joss Whittle is right, and Siraj comment startled me as well first time I watched. The derivative always points to the direction of the (possibly local) maximum. The gradient definition used in the context of backprop is not different. Even though in HOG it does not represent an error to be minimized, the property still holds.
Yes one is gradient as in describing a slope, the other is gradient as in color. I think thats what he means by different 🙂
Gotta send a link of this to my ex-wife! Maybe she can finally detect that I am a person.
Way to much info to much but it’s good your venting.
Never marry a lizard person
haha wow thats real af
#LIZARD PERSON REALLY?/@#
Lol, i wish in future it can detect and read mind
These videos are great! also a lot easier to focus on when there aren’t memes popping up all the time. I enjoy the lecture style.
thanks Carl noted
10/10 for this. I’d never heard of YOLO, and this is a really great introduction.
I was looking for this just a few days ago and was a great coincidence that you decided to upload this video , thanks!!
Siraj! Thank you so much! When you explain step by step like this I can undestand everything! Love this video!
I thank God, that I started studying programming/math, so much fun and so fascinating to be able to take part in such cool technological advancements.
Your videos are so amazing. You cover all the fields of CS practically, with a state of the art approach.
So helpful, keep it up
I’ve seen YOLO before about a year or two ago it seems like it got better even since then. Good to see them still improving their product.
TBH, I only clicked this because it said YOLO. Now my brain is exploding.
But joking aside, you’re a great explainer and this is all starting to make sense. Thanks for the video!
The whole video is very thorough and comprehensive, which makes such intimidating subject a no-brainer for the beginners. Not sure how I will use YOLO in my future projects, but I really learned a lot from this video!
Its been five years. How about now?
For videos, I think the algorithms should take the time dimension into account, (ie. increasing the probability of an object detected in one frame to be there again in the next frame) to decrease computation cost.
Man! You are amazing. your kind of presentation makes me stay completely focused!
Thanks for your work it is the first time I find proper and clear explanations about how to interpret the network output!
You sir, are the reason my company is headed into softwsee development, coding, and programming. This video is worth more than gold.
That was an excellent description of a topic that has been confusing the heck out of me for many hours. Thank you!
CNN works this time
1- Computation
2- Large Amount of Image available