Official YOLOv7 Pose vs MediaPipe | Full comparison of real-time Pose Estimation | Which is Faster?

YOLOv7 Pose estimation vs. MediaPipe: Comparison for Human Pose Estimation.
In this video, we make an extensive comparison of YOLOv7-Pose vs. MediaPipe
Our Conclusion:
-YOLOv7 is a multi-person detection framework. MediaPipe can not beat YOLOv7 in this category.
-MediaPipe is observed to be producing good results on low-resolution inputs compared to YOLOv7.
-It is faster than YOLOv7 on CPU inference.
-MediaPipe is also comparatively good at detecting far-away objects (persons in our case). However, when it comes to occlusion, YOLOv7 wins.
-Moreover, YOLOv7 can harness the power of GPU.
In this video, you will get answers to the following questions
✅What is YOLOv7 Pose?
✅What is MediaPipe Pose?
✅How is YOLOv7 Pose different from MediaPipe?
✅How YOLOv7 Pose compares to MediaPipe?
❓FAQ
How does human Pose Estimation work?
What is MediaPipe Pose Estimation?
Can MediaPipe detect multiple people?
What is human pose estimation?
⭐️ Time Stamps:⭐️
0:00-00:30: Introduction
00:30-01:22: What is Human Pose Estimation
01:22-01:50: Applications of Human Pose Estimation
01:50-01:58: Popular Algorithms of Human Pose Estimation
01:58-03:18: What is YOLO Pose
03:18-03:35: YOLO Pose Architecture
03:35-03:46: YOLOv7 Architecture
03:46-04:04: MediaPipe
04:04-04:09: YOLOv7-Pose vs MediaPipe
04:09- 08:01: Result Comparison between YOLOv7-Pose and MediaPipe
08:01- 09:09: Summary
Resources:
📚 Blog post Link: learnopencv.co...
🎵 YOLO Master Class Playlist:
• YOLO Master Class: Mas...
🖥️ On our blog - learnopencv.com we also share tutorials and code on topics like Image Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.
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🔖Hashtags🔖
#mediapipe #Yolov7 #poseestimation #machinelearning #objectdetection #deeplearning #computervision #learnopencv #opencv

[@LearnOpenCV]

Get expert guidance, insider tips n tricks and Create stunning images, learn to fine tune diffusion models, advanced Image Editing techniques like In-Painting, Instruct Pix2Pix and many more. Join our Kickstarter campaign now! bit.ly/3JYh7A6

[@NakulYadav-jf9wn]

R Ttf P P

[@LearnOpenCV]

📚 LINK TO BLOGPOST: learnopencv.com/yolov7-pose-vs-mediapipe-in-human-pose-estimation/ ▶ LINK TO YOLO MASTERCLASS PLAYLIST: kzbin.info/aero/PLfYPZalDvZDLALsG9o-cjwNelh-oW9Xc4

[@johncasey434]

It was a real pleasure to watch such a clear and concise comparison. Excellent video 👍

[@LearnOpenCV]

Glad you liked it @John. More videos incoming!

[@nvmrenh7938]

Great video comparisson between Yolov and Mediapipe man, good thing I saw this video in my KZbin feed. +1 Sub 👍

[@LearnOpenCV]

Awesome, thank you!

[@leonidas1983]

great explanation! thanks from Argentina

[@murcuschimaawaloyi6619]

Very good explanation. Hi Sir. I have been following your tutorial on how to train a custom Yolov5 object detector as I am doing a school project on vehicle detection. I am having an error on training my model. Is it ok if you can help on this please.

[@rahulagiwal4126]

Great Video!! Thank you for the super informative video, was looking for the right pose estimation to use for my dance project and this really helped!

[@LearnOpenCV]

Glad it was helpful!

[@yohanessatria2220]

Nice Video! the test on many cases was so helpful!

[@LearnOpenCV]

Thank you Yohanes!

[@leonidas1983]

great work, thanks!

[@nicopetermann1851]

Many thanks for this great video! You mentioned that one can use any object detection model for yolo pose - could you elaborate on that? How could one plug in the smallest version of yolov7?

[@LearnOpenCV]

You would need to retrain the network with a different backbone. The authors have trained it for the YOLOv7-W6 model. You can train the model using a different yolov7 model. What you would need is a config (.yaml) file corresponding to the smaller model. You can then train the model using the commands given here: github.com/WongKinYiu/yolov7/tree/pose I doubt it would give accurate results for smaller models. I would use mediapipe if I don't need multi-person pose estimation.

[@ChetanAnnam]

Awesome comparison, it reduced my work drastically.

[@LearnOpenCV]

We felt the same while working with both YOLOv7 and mediapipe that everyone should know about this comparison! Glad you found it useful.

[@siddharthkumar5206]

Mediapipe does support multiperson detection now

[@shoghi2547]

I like your sharing. It is clear and easy to understand.

[@LearnOpenCV]

Thank you, glad you liked it 😊

[@nhattuyen1123]

thank you so much, this video is very helpful

[@LearnOpenCV]

Glad it is helpful!

[@GeoffY2020]

Hi thanks for the nice job in the video ... I'm doing single image (3 image consecutive) face landmarks alignment, is Yolo better than MP ?

[@LearnOpenCV]

Thanks for the kind words Geoff! YOLO does not have good enough number of points for Face landmarks alignment. Mediapipe has a dedicated face mesh model that gives 468 3D landmark points on the face. You can check out our blog post on Creating Snapchat filters using mediapipe. You can learn about how to use the different points for your application. learnopencv.com/create-snapchat-instagram-filters-using-mediapipe/

[@Favourites_Song]

Great Video sir. Thank you for sharing.

[@LearnOpenCV]

You are very welcome

[@nirasinghania6616]

👍👍

[@LearnOpenCV]

Thank You!

[@abdurrazzak1612]

Excellent

[@LearnOpenCV]

Thank you. Glad you liked it.

[@lucho_oaaa8775]

nice video

[@LearnOpenCV]

Thank you so much!

[@maximklechshev6675]

I felt in love with Mediapipe 1 year ago when I worked with facial pose estimation… but YOLOv7 just outperforms it in terms of faces

[@LearnOpenCV]

Hi Maxim Are you talking about face Detection or Facial Landmarks Detection using YOLOv7?

[@maximklechshev6675]

Hey! I’m talking about Facial Landmarks Detection. I fine-tuned and used ensemble instead

[@LearnOpenCV]

Great, do you have a repo you could share?

[@maximklechshev6675]

@@LearnOpenCV I worked with medical sensitive data(

[@LearnOpenCV]

No Issues!

[@rohitghule9437]

Can we tweek mediapipe to work even when upper part of body is not visible

[@LearnOpenCV]

The pose solution model consists of two models. The detection model (that detects the body), and the landmark model (that maps the landmarks). If you can make the detection model detect the body without its upper part, theoretically, the solution will work.

[@nireksaravanan8532]

Are you sure Mediapipe doesn't support Multi-person? pls verify once

[@LearnOpenCV]

As of 2024 Jan update, Mediapipe does supports mutiperson pose but limited to 5 at a time. For further info check out: developers.google.com/mediapipe/solutions/vision/pose_landmarker/

[@H_-vy2mz]

호호

[@LearnOpenCV]

I'm not sure what that means, but I'm hoping you liked it! 😊

[@dj.qb91]

What about in images

[@LearnOpenCV]

As mentioned in the summary section, it's better to use YOLOv7 or other pose models as mediapipe is optimized for real-time performance which is more suitable for video inference. Hope that helps!

[@dj.qb91]

@@LearnOpenCV so with Multiperson which is better than yoloV7.

[@LearnOpenCV]

@@dj.qb91 For Multiperson we're checking out MMPose next -> github.com/open-mmlab/mmpose. You may also check it out and compare with YOLOv7. Check this out for getting started: mmpose.readthedocs.io/en/v0.29.0/get_started.html#inference-with-pre-trained-models

[@dj.qb91]

@@LearnOpenCV thanks 🙏🏾