2017 RetinaNet paper summary

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  • čas přidán 29. 08. 2024

Komentáře • 28

  • @tbyte007
    @tbyte007 Před 2 lety +3

    This channel is seriously underrated. This is the second video I watch and both have all I need to get the idea !

  • @nagamanigonthina9306
    @nagamanigonthina9306 Před 8 dny

    Thank you so much. The video is very clearly explained in detail. It is easy to understand.

  • @jaisbenny1839
    @jaisbenny1839 Před 4 měsíci

    Thank you so much for making our lives easier.

  • @Mejayy
    @Mejayy Před 5 měsíci

    Very useful thank you! By the way, that "r" in the focal loss function is the greek "gamma" letter

  • @aisolutions834
    @aisolutions834 Před 3 lety

    Awesome, thank you for breaking down the easy and hard examples and how the easy noises add up and overwhelm the model!

  • @loserc1854
    @loserc1854 Před 6 měsíci

    很清晰,帮助我理解

  • @NehadHirmiz
    @NehadHirmiz Před 3 lety +1

    Thank you for this excellent explanation

  • @inkypie
    @inkypie Před 3 lety

    Fantastic videos! I watched all of your videos and I hope to use them in my work. Thank you!

  • @nathanielscreativecollecti6392

    Me: Hmm... I need to read a paper for work, what does CZcams have to say about it.
    Hao: Here you go!
    Me: "Well that was super easy, barely an inconvenience."

  • @elieeid5479
    @elieeid5479 Před 3 lety

    this video is one of the best explanation available about the Retinanet, and also other videos are as great as this one !!
    Could you please do a similar one about Retinamak or Mask-RCNN it would be great

  • @tusharkantdeo
    @tusharkantdeo Před 3 lety

    I found your videos very informative and easy to understand....Thank you :)

  • @bilelkhelifi897
    @bilelkhelifi897 Před rokem

    Awesome exaplanation, Thank you so much

  • @dicksonneoh5563
    @dicksonneoh5563 Před 3 lety +1

    Thank you for this!

  • @eliordadon2938
    @eliordadon2938 Před 6 měsíci

    Bro, breath more or something it sounds like you're about to puke. Awesome explanation! loved it

  • @mudasirulhaque4873
    @mudasirulhaque4873 Před 2 lety

    Great video. 1 question tho did they try to balance the easy and hard examples by weighing scheme in this case maybe by using IOU between combinations of boxes. Or maybe thats too much computational expensive ?. Thanks for the explanation i always see this video for reference good stuff. ❤️

  • @louisrose7823
    @louisrose7823 Před 2 lety

    Great explanation !

  • @bobochen2435
    @bobochen2435 Před rokem

    thanks bro!

  • @kndlt
    @kndlt Před 5 měsíci

    why so low volume on this video

  • @meetshah2288
    @meetshah2288 Před 2 lety

    loved it. keep going

  • @codeWithBatta
    @codeWithBatta Před 3 lety

    I was going through your yolo4 explanation. I need you to enable the playback speed option in that video. I would be thankful if you can do it.

  • @ashishkumarchoubey5592

    Great bro.

  • @nebenmensch5325
    @nebenmensch5325 Před 3 lety

    What do you prefer more? YOLO or SSD.

  • @usmaniyaz1059
    @usmaniyaz1059 Před 3 lety

    Kindly tell me what is AP50 and AP75?

    • @haotsui2720
      @haotsui2720  Před 3 lety

      This article might help you: jonathan-hui.medium.com/map-mean-average-precision-for-object-detection-45c121a31173

    • @usmabhatt1768
      @usmabhatt1768 Před 3 lety

      Great. Thank you so much

  • @borisepshtein
    @borisepshtein Před 9 měsíci

    Learn English, pal. It's not "imbalance data", it's "imbalanceD" data. Also, pay attention to plurals.