I can’t distinguish the differences between YOLO and Convolutional implementation of Sliding Window; Except YOLO states a convenient number for slices or grids to divide the image into (number of output pixels regardless of channels (landmarks, #number of classes, …)). Is YOLO another name for Conv…

Here have a look on these threads which describe the same question: [image] YOLO - How does Bounding box get identified when Object spawns multiple sliding windows(Grids) Convolutional Neural Networks Hello Mentor Team, Good day!! I have been trying hard to visualize the…

That is not helpful actually! My question lies on Convolution Sliding Window and not Sliding Window and it is not the same question as I had a glance at those threads. But thanks anyway…

Going through convnet course now. Even I cant understand difference between them given that in convolutional implementation of sliding window we pass entire image in one iteration itself. Got any clarifications about it yet?

While I have read that OverFeat paper referenced by Prof Ng in the discussion of Convolutional Implementation of Sliding Windows (CISW), but I haven’t ever worked with an implementation of it. I have spent a lot of time working to understand YOLO and implementing it myself from scratch. Here is my …

After going through the YOLO V1 paper and the Convolutional implementation of Sliding Window (CISW) paper, here is my take about YOLO V1: It does NOT apply sliding window. Instead, it’s a Conv + FC net that tries to predict bounding boxes in a 7x7 grid, that’s it. Each grid cell will detect the exi…

[image] RicoRuotongJia: [YOLO v1] … does NOT apply sliding window. Thanks for that observation. What about the architecture of OverFeat ? I thought the point of the Convolutional Implementation of…’ part was that it also does NOT apply sliding window, at least in the traditional sense of runn…

[image] ai_curious: I thought the point of the Convolutional Implementation of…’ part was that it also does NOT apply sliding window, at least in the traditional sense of running forward propagation sequentially on input subregions. This is a great question. My understanding is that in OverFe…

[image] RicoRuotongJia: YOLO V1 however, can output window of any size (the network will learn the window size from the training data) ? maybe can output predicted bounding box of any size ? Not clear to me what is meant by learned output window otherwise, as YOLO v1 grid size is static and…

[image] ai_curious: maybe can output predicted bounding box of any size ? Not clear to me what is meant by learned output window otherwise, as YOLO v1 grid size is static and not learned by an algorithm. Great question. Yolo v1 grid size is static, but the output of one grid cell contains the…

[image] RicoRuotongJia: so OverFeat has no localization regression layer, I think we’re pretty much in agreement except for this statement above. See the Overfeat paper section… 4 Localization Starting from our classification-trained network, we replace the classifier layers by a regression…

YOLO vs Convolutional Sliding Window

Course Q&A Deep Learning Specialization Convolutional Neural Networks

gent.spah February 3, 2024, 11:17am 2

Here have a look on these threads which describe the same question:

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YOLO vs Convolutional Sliding Window

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