Can you tell me how the yolov8 architecture works? I would like to ask how to train the model to recognize circles and output, instead of the width and height of the bounding quadrangle, the x/y location and radius.
Hello thank you for your guidance. But just wondering is there any way to open the phone's back camera and show the predictions with bounding boxes in realtime with the stream directly on the web app. Currently I could only do that on laptop web cam.
Your work is truly exceptional, and I greatly admire it. I'm optimistic that, in time, you will skillfully implement animal 3D pose estimation using YOLO. Keep up the remarkable work!
hi. after following the instance segmentation tutorial with yolov8, I found that the train mode output contained one confusion matrix. My question is, does the CM belong to the box or the mask? Thank You
Hello, nice video! When I try to replicate the project, by doing the following: from utils.general import check_img_size from models.experimental import attempt_load It doesn't seem to recognize those files and it shows the following: Import "utils.general" could not be resolved(reportMissingImports) Import "models.experimental" could not be resolved(reportMissingImports) How can it be solved? Because after executing that block of code, it no longer allows to make a correct plot_image Can you help me please?
How about if I make a custom model to find fruit bugs/insects and once the model finds one I would like to also find which fruit is this bug/insect on? Would I have to use 2 different models, one custom and the default and just send the analysis from one to the other? Or can I append learning to an already built model so it can learn fruit bugs/insects?
Good afternoon, how are you? As I mentioned to you in the video, I have a very big need in this segment. I serve a city, schools, health centers, and we really need to develop for these segments. Would you be interested in making a partnership, I'm sure it will be very productive, and a great experience. I have everything I need ideas, if you are interested let me know. Success for you.
@Skalski, have u ever trained keypoint detection with other architecture (OpenPose, pifpaf, etc) but with custom dataset? I'm sry if my question is a little off topic hehe....
Awesome! Thank you for sharing your code :) Can I ask you which camera did you use? I want to try your project too, and I wonder each scene matches in same timestamp when you put two videos for estimating correct 3d data.
Thanks a lot. I'm doing my best, but I have a lot to do at work. If you want to watch my videos, you can do it on the Roboflow channel: www.youtube.com/@Roboflow. I usually produce one video a week there.
@@SkalskiP Haha! Good answer :). I'm interested in the possibility of taking this to a real time solution using 2 synced cameras and a fairly standard laptop (with GPU)
Great work! I have been working on a very similar project, but done live for multiusers at around 15fps. I was wondering how do you fuse the info from the 2 cameras in the 3d model? Do you know the position of each camera? Or one camera relative to the other?
@@SkalskiP it would be great to have some details on your 3D modeling for positioning the 17 joints in space. I understand you have some relative solution, where you preset the size (height) of the model to 1000. I am interested in a 3D positioning in space of the joints with a given coordinate origin. I was wondering if you have some insights or thoughts on that.
@@asdfds6752 Yes I try to calibrate both models to the same pre set dimensions. I’ll try to include that on blog. I should be sharing that link this week on LI.
great tutorial, i've been making a project about human pose estimation (just in case of study) and i am stucking in this field about yolov7 right now, i really need some help, how can i contact you
@@SkalskiP tracking the object, for example, if person is captured in a camera lets say he has ID 1, he should be tracked by other camera with same ID.
Thank you! This is actually very good question. With current setup I got 3 FPS on Tesla T4, so we would need to get a lot more efficient. But I think that combination of smaller model and more powerful GPU could bring us to 20 FPS
Thank you very much :) Feel free to subscribe. New Kaggle competition is up: www.kaggle.com/competitions/nfl-player-contact-detection and I'll most likely drop some video about it soon :)
