The audio sucks but this man knows what he's talking about. I was taking Andrew Ng's deep learning course which confused the hell out of me and these videos made it much clearer! Can you maybe produce a video explaining the training of the model. Something which would explain the input features.
You mention the metric as "Union over Intersection"? By the formula you mentioned, I'm pretty sure the metric is "Intersection over Union" as the latter makes sense from the division. Do think about this or let me know if the former one is actually also in place.
Such a clear explaination ! but I want to make sure that what I understood is correct so here's my understanding and doubts: 1. we divide image into S x S grid 2. In each grid, we try to predict probability that the bounding box(which we are predicting from our model) contains an object or not 3. With 2, we try to predict the coordinates of the bounding box and the respctive conditional probabilities of classes 4. Step 2,3 is I suppose the output of the model w.r.t each grid but I am still confused that if B is no of bounding boxes which we want to predict then why do we need 5B+C vectors?
i think 5B+C is the lenght of the y vector, so if B = 2 then the y vector needs 5 elements for p,x,y,h,w of the first bounding box, then p,x,y,h,w for the second bounding box and lastly C elements for the probability of each class, 5*2 + C
The content is one thing, knowing what to say is other but you need to master how present the information and how you speak, sound quality is really bad. But I like the content. Thanks.
Anchor boxes are nothing but initial guesses of the bounding boxes, calculated using the aspect ratios and sizes of bounding boxes in the training dataset
Hi, All!. Thank you for this good video, but I'm wondering why the formula is S*S*(5*B+C), because according to this ru-vid.com/video/%D0%B2%D0%B8%D0%B4%D0%B5%D0%BE-vRqSO6RsptU.html the formula should be S*S*B*(5+C). Can you elaborate on that?
@@tulliolevichivita5130 Hi! Here's what I interpreted from the video. SxS refers to the number of grids initially defined. For each of those grids there is a certain amount of Bounding Boxes (B) defined by p_c, b_h, b_w, b_x, b_y (5 params) and the probabilities of each bounding box belonging to the different classes (C). I think the second formula is the right one, as it makes no sense defining bounding boxes and not clasifying the object in it.
