YOLO v7 Object Detection Models: FPS & Object Detection Comparison (All 7 Models)

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  • เผยแพร่เมื่อ 27 พ.ย. 2024
  • YOLO v7 object detection model has 7 variants. This video showcases FPS and objects detection accuracy of all the models. There is a tradeoff between speed and accuracy, and this comparison is helpful in deciding the sweet spot. Which model variation works best for your case? Let me know in the comments down below.
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    ► My Tutorials:
    ○ YOLOv7 Custom Object Detection (Win, Linux): • Official YOLO v7 Custo...
    ○ YOLO v7 Object Detection Complete Tutorial:
    • Official YOLO v7 COMPL...
    ○ TensorFlow Object Detection Complete Tutorial: • TensorFlow Object Dete...
    ○ Realtime Object Detection Using OpenCV Python ON CPU: • Realtime Object Detect...
    ○ DETECTRON2 Custom Object Detection, Custom Instance Segmentation Tutorial: Part 2 (Training Models): • DETECTRON2 Custom Obje...
    ○ DETECTRON2 Custom Object Detection, Custom Instance Segmentation Tutorial: Part 1 (Preparing Data): • DETECTRON2 Custom Obje...
    ○ Detectron2 on Colab: • DETECTRON2 TUTORIAL fo...
    ○ Instance Segmentation as Rendering: • DETECTRON2 PointRend T...
    ○ Detectron2 Complete Tutorial: • COMPLETE DETECTRON2 TU...
    ○ YOLOv4 On Android Using TFLite: • YOLOv4 TFLite Object D...
    ○ Custom YOLOv4 Object Detection with TensorFlow and TFLite : • Custom Object Detectio...
    ○ Darknet YOLOv4 Custom Object Detection: Part 2 (Training YOLOv4 Darknet): • YOLOv4 Custom Object D...
    ○ Darknet YOLOv4 Custom Object Detection: Part 1 (Preparing Custom Dataset): • YOLOv4 Custom Object D...
    ○ YOLOv4 Object Detection with TensorFlow, TFLite and TensorRT: • YOLOv4 Object Detectio...
    ○ Darknet YOLOv4 Object Detection for Windows 10 on Images, Videos, and Webcams: • Darknet YOLOv4 Object ...
    ○ Real-Time Object Detection on Webcam and Videos Using OpenCV With YOLOv3 and YOLOv4 | Windows Linux: • YOLOv4 and YOLOv3 Obje...
    ○ Real-time Multiple Object Tracking with YOLOv4 TensorFlow and Deep Sort | Linux, Windows: • Object Tracking with T...
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ความคิดเห็น • 35

  • @felipewitezak9130
    @felipewitezak9130 2 ปีที่แล้ว +1

    God job! Make a compare with others yolos !!!!!!

  • @furkantoptan6767
    @furkantoptan6767 ปีที่แล้ว +2

    On which device did you get these fps values?

  • @kiena26
    @kiena26 ปีที่แล้ว

    Thanks for sharing~!

  • @ZionZhou-k8y
    @ZionZhou-k8y ปีที่แล้ว +1

    Hello! I'm really curious about the resolution information for these videos. Thank you for your help!

  • @senayuksel3342
    @senayuksel3342 ปีที่แล้ว +1

    In your codes, did you use any framework especially like tensorflow lite?

    • @TheCodingBug
      @TheCodingBug  ปีที่แล้ว

      In this video, no. But I have yolov4 video on my channel where I convert yolov4 to tflite and use it in mobile application.

    • @senayuksel3342
      @senayuksel3342 ปีที่แล้ว

      @@TheCodingBug thank you for your feedback

    • @senayuksel3342
      @senayuksel3342 ปีที่แล้ว

      @@TheCodingBug With your permission, I would like to ask one more question. I am using jetson nano and fps is very important to me. I want to use tensorflow lite. Which YOLO version do you think would be more useful for me?

    • @TheCodingBug
      @TheCodingBug  ปีที่แล้ว

      @@senayuksel3342 yolov8 nano or yolo-nas

  • @mizantednioc
    @mizantednioc ปีที่แล้ว +1

    Are you running your test using an IP Camera or an mp4? yolov7 on my machine is fast only with my webcam

  • @kin_1997
    @kin_1997 2 ปีที่แล้ว +1

    cool video, though i am not sure which is better here?

    • @TheCodingBug
      @TheCodingBug  2 ปีที่แล้ว

      It depends on the input. From top view, larger models are better.
      From street view, YOLOv7 is better due to FPS.

  • @MEATHEADBooYA
    @MEATHEADBooYA 2 ปีที่แล้ว +1

    Hello again, I have a random question. So I am trying to train on Yolov4 following all your tutorials and its working as intended! However I just cant train with a width and height of 1024.
    My settings are
    Batch = 64
    Sub = 64
    Width = 1024
    Height = 1024
    Running on a 3060 12G. 16G of Ram and 16 core AMD 3650x
    I am told I need to have 1024 width and height as that is what the pre-trained / Images are on.
    What I get is
    1472 x 1472
    Create 6 permanent CPU-threads
    Error: cuDNN isn't found FWD algo for convolution
    I have looked everywhere but I don't see anyone training at 1024 even though that is what my images are on.
    What I also notice is that my GPU Memory goes to 11.4G - 11.6G of the 12G and then the Error appears.
    Would love some help.
    Thanks
    UPDATE - Ok so I just noticed that my compute_capability is only 860.

    • @vvv228
      @vvv228 2 ปีที่แล้ว

      try batch 32

  • @andrespereira4852
    @andrespereira4852 2 ปีที่แล้ว

    Hi Mr TheCodingBug. I like a lot your channels for all the well explained content that you were creating all this time. Now, back to this video... what is the hardware specs that you used to test the 7 models?

    • @TheCodingBug
      @TheCodingBug  2 ปีที่แล้ว +3

      I am using GTX1060 6GB for all of these runs.

  • @chaimacham9414
    @chaimacham9414 ปีที่แล้ว

    please how can i improve my fps my yolov7 model when i run it with my custom dataset show 4fps and it is really low what can i do ?

  • @rahmadyd
    @rahmadyd ปีที่แล้ว

    can u show how to display fps on screen? thx

  • @MEATHEADBooYA
    @MEATHEADBooYA 2 ปีที่แล้ว +1

    I was speaking with some people and they claimed that Yolov7 is not as good as Yolov4. I think I will stick with Yolov4 myself.

    • @TheCodingBug
      @TheCodingBug  2 ปีที่แล้ว +1

      Thats interesting. I have not compared these two myself so I cannot say for sure. As per research paper, the YOLOv7 is faster and have higher mAP on COCO dataset.
      The darkent based base YOLOv4 was giving an FPS of up to 13 (th-cam.com/video/FE2GBeKuqpc/w-d-xo.html), while base YOLOv7 is giving up to 16FPS. So it's some improvement there that we can notice, keeping in mind that darknet is based on C++ and requires a lot of effort to setup.

    • @MEATHEADBooYA
      @MEATHEADBooYA 2 ปีที่แล้ว +1

      @@TheCodingBug Yeah I think that is the issue, the setup for YOLOv4 and Darknet. Will you be doing an extended tutorial on training with Yolov7? :)

    • @TheCodingBug
      @TheCodingBug  2 ปีที่แล้ว +2

      @@MEATHEADBooYA Yes. I am working on custom dataset tutorial for YOLOv7.

  • @NevaEdizUfuk
    @NevaEdizUfuk 2 ปีที่แล้ว +1

    we need custom object detection on python 3.9 and last version tensorflow please you haven't like video !

  • @pearcexx6085
    @pearcexx6085 ปีที่แล้ว

    How can you demonstrate the FPS values ?

    • @TheCodingBug
      @TheCodingBug  ปีที่แล้ว

      Higher FPS means the model variation is faster.

    • @pearcexx6085
      @pearcexx6085 ปีที่แล้ว

      I mean how you were able to show these fps values on window ?

    • @pearcexx6085
      @pearcexx6085 ปีที่แล้ว

      ?

    • @TheCodingBug
      @TheCodingBug  ปีที่แล้ว +2

      @@pearcexx6085 Modify detect.py as mentioned in my other tutorial: th-cam.com/video/_CkXDjmT8dc/w-d-xo.html

    • @pearcexx6085
      @pearcexx6085 ปีที่แล้ว

      Appreciated 🙏🏻

  • @humanized5614
    @humanized5614 2 ปีที่แล้ว +2

    Can u try demonstrating a custom dataset training in kaggle? Kaggle is better than google colab as it gives u 30 gpu hours per week and doesn’t restart like google colab
    Also can u show us how to implent Yolov7-tiny and a python code demo?

    • @TheCodingBug
      @TheCodingBug  2 ปีที่แล้ว +2

      Python demo for all variations of YOLO v7 can found here:
      ○ YOLOv7 Custom Object Detection (Win, Linux): th-cam.com/video/-QWxJ0j9EY8/w-d-xo.html
      ○ YOLOv7 Complete Tutorial (Colab): th-cam.com/video/_CkXDjmT8dc/w-d-xo.html
      ○ YOLOv7 Complete Tutorial (Windows, Linux): th-cam.com/video/n2mupnfIuFY/w-d-xo.html
      I will try custom object detection on Kaggle.