Thanks for such a good video. Your explanation was more detailed and understandable than others'. But I'm a bit confused how goes second stage. I would be happy if you clarify or correct my understanding: As far as I understand, in the second stage model contains from CNN, RPN and Fast R-CNN. Then we train CNN and Fast R-CNN but we freeze RPN. RPN just generates regions but RPN weights are frozen in the second stage, am I right?
@Explaining-AI
3 ай бұрын
So what you mentioned is one of the ways of training. There are three approaches training depending on if the backbone is shared or not. 1. Training Faster RCNN where RPN and FastRCNN models have separate backbone(lower performance). Here, we first train RPN_Backbone + RPN in Stage I to have a model capable of generating regions. Then in Stage II we train FastRCNN_Backbone(different from Stage I) + FastRCNN using proposals from already trained RPN(here RPN is now frozen and not trained in Stage II). 2. Joint training with shared backbone where Backbone + RPN + FastRCNN, all are trained together. RPN classification and localization loss is used to update parameters of Backbone + RPN FastRCNN classification and localization loss is used to update parameters of Backbone + FastRCNN 3. 4 Step Alternate training with shared backbone This is talked about in the video @25:23 Stage I - RPN + Backbone trained Stage II - Fast RCNN + Backbone trained(RPN frozen) Stage III - RPN Fine tuned(Backbone frozen) Stage IV - FastRCNN fine tuned(RPN and Backbone frozen) Do Let me know if you have more questions around this.
@adammonroe378
3 ай бұрын
@@Explaining-AI Yeah now it's more clear! Thank you for your answer!
@sushilkhadka8069
2 ай бұрын
Hi I really like your videos. Thanks for all the effort. I have a question, why do they use foreground and background classification, what's the significance of classifying a proposed region into fg and bg? At the end of the day we're interested to classify that box as a region proposal or not right?
@Explaining-AI
2 ай бұрын
Hello, Thank you! Regarding your question, the purpose of RPN is to perform the responsibility of proposing regions. Using anchor boxes we have divided image into different regions, now these are not proposals that potentially contain an object, these are just regions of image. RPN needs to learn the foreground and background classification on these regions(whether this region contain an object or not) to be able to pass only high scoring valid proposals(those which have high foreground class probability, out of all anchor boxes) to detection layers. Do let me know if I have somehow misunderstood your question.
@DestinedToWin27
3 ай бұрын
Thank you so much for the video. This is the best explanation of RCNN model family so far on KZitem. You should continue this series with topics like mask RCNN etc.
@Explaining-AI
3 ай бұрын
Thank You. Yes will definitely cover that. I might do a different series on Instance Segmentation covering MaskRCNN YoloAct e.t.c but will indeed cover MaskRCNN in one of the videos.
@DestinedToWin27
3 ай бұрын
@@Explaining-AI Thank you😊
@amirhosseinizadi7125
2 ай бұрын
@@Explaining-AI when mask rcnn and yolo drop?
@Explaining-AI
2 ай бұрын
@@amirhosseinizadi7125 Currently I am working on ControlNet video(should finish that in 3-4 days), and after that will do one on Yolo. MaskRCNN would take some time as that will be part of a different series which I havent started working on yet.
@ameliapuspa7230
4 ай бұрын
such a good explanation and illustration! Please make about YOLO
@Explaining-AI
4 ай бұрын
Thank You! Yes, as part of this series, will be making videos on different versions of YOLO as well.
@samt5682
3 ай бұрын
Hello will you implement it from scratch especially in tensorflow? Plus i have been struggling a bit with yolo v2 implementation. Would also need help on that😅
@Explaining-AI
3 ай бұрын
Hello, Actually I have limited experience(and by limited I mean zero) with tensorflow, so the implementation would be in Pytorch. That video should be out in 3-4 days and my hope is that it will be of some help to you, using which you could get a better understanding of tensorflow implementation as well. And yes this series will indeed have videos on different yolo versions . Would take some time to get through all of them but it will have it.
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