I get this error: "...too many values to unpack (expected 4)" pointing inside the labels array. Why am I getting this error this error? What should I do to fix it? :) pip list: ...numpy 1.24.2, torch 2.0.0, torchvision 0.15.1, ultralytics 8.0.82, supervision 0.6.0

Your content has become the best content I've watched on YT in a while and I love the Supervision package, it's making my work easier. Thanks

omg thank you so much, everyone is using yolo on it's own and not with OpenCV and that's exactly what I needed

It's so cool, thank you for showing basic function to work with YOLO, i'm having a small task that required learning object detection and I was frustrated to find some tutorial, this was really a big help

Well explained @Peter. Useful and informative video that can cover multiple use-cases. Thanks a lot!

Hi, your video is great. I've been using your code with my own model to count bacteria inside the zone and it works perfectly! Thank you for sharing the knowledge.

Hi, thank you for the tutorial. I'm working on my project right now and it helps me a lot. But this project require me to know the fps, is there a way to show it?

Great, thanks for this. Keep doing.

God bless you, you have made my life much easier. Keep up the good work

it was great to see how easy is it to remove a class from detections! Great job @roboflow!😁

thank you for this video! very helpful. but i am a problem. the line "import cv2" in not identified what should i do?

Hi Peter. I have a question regarding filtering detections. Filtering sv.Detections is the same as passing class ids as additional argument in the predict method or there are some performance issue with the second alternative?

Thanks for your complete step by step coding video. Kinna like it. I followed yolo since v3, but never know we can filter out hand or other unrelated object to be detected

Great video sir....!. Mass respect from India ...!

That's a fantastic tutorial! Thank you so much! I have a question: if it's possible for you to guide me on how to implement re-identification (or maybe re-tracking) of the same object with YOLOv8?

That was great, thanks!

When the number of objects increases, my model does not display the label format that I specified and only displays the object code, how can I display the specified label in any case?

Is it possible to do multiple polygon zones inside 1 frame?

Thank you so much for this amazing tutorial! I have a question: I'm interested in extracting the results from a frame like the result, specifically the count of objects and their corresponding types, and then outputting them in JSON format. Do you have any suggestions or ideas on how I can accomplish this?

Is it possible I change the "zone_annotator = sv.PolygonZoneAnnotator(zone=zone,color=sv.Color.white(),thickness=2,text_thickness=4,text_scale=2)" position. For example, display the red box with the object number on the left/bottom side on the screen?

Impressive mate!

Hello , thank you for great video. I would like to know if I can save the results as a time-stamped data in csv format. If you respond, it makes me pleasure. Best regards

Exlente video he aprendido mucho

Great stuff Piotr!!!

Thank you so much bro.... Very helpful 🙂👍

Always love your content and its small jokes when things go wrong. Thank you! 👍++

What version of Ubuntu and ros are using in your video ?❤

How to use it with custom class?

these videos are amazing

thank you !

Awesome!

Awesome!!

Master piece

Hi @SkalskiP , that was a wonderful explanation. Is it possible to track the objects that comes in or goes out using a polygon zone like you did using a line in your earlier video? If yes, how can i get the count of objects (in/out) separately for each class.

dont know what version of python you are using. im using 3.9 and i cant install specific version of ultralytics

I absolutely love your videos! YOLO is indeed amazing. But I do have a question: How do I do it so that I can only detect and count people (whether it's a webcam feed or a video)?

hi how can i desactivite de log in the console ?? like exaple all this "0: 384x640 1 person, 1 chair, 4 potted plants, 1 bed, 37.0ms Speed: 1.0ms preprocess, 37.0ms inference, 5.0ms postprocess per image at shape (1, 3, 640, 640) 0: 384x640 1 person, 1 chair, 4 potted plants, 1 bed, 35.0ms Speed: 2.0ms preprocess, 35.0ms inference, 3.0ms postprocess per image at shape (1, 3, 640, 640)" thank you !

Hello sir, How can I develop real-time webcam functionality using a dataset I've created?

I keep getting 2 detections for the same class even after writing agnostic_nms=True is there a way to say: Just detect 1 of each class in the whole window, the one with higher confidence?

I have an error: i caanot use boxannotator and detections it says unused reference initpy

any idea how to freeze the other classes and take only one class

how about IP carmera? opencv read via RSTP is very slow.

Thank you for the video Piotr! How handle multiple camera detections and counting in zone and save results in database. Can you make a video tutorial about it

Thank you for video! I'm curious about how to write the code to load an ONNX model. I would like to load an ONNX model, but I'm not sure how to do it. When using the code from the video, I encounter an input size error. Are there any helpful videos or resources that I can refer to in this case?

I use Linux/Ubuntu as it looks like you're doing.. :) Would you say that within the ML/Vision industry the Linux platform is the most common? I rarely see Windows being used as the platform of choice for this application.

@SkalskiP Hi Peter, what operating system are you using?

Is it possible to use the YoloV8 model in .onnx format rather than .pt, for real-time object detection? I only ask thinking the detection/prediction should take less time. And if so, would you be able to make such a video? :)

is there any way of taking only 4 labels, for example, truck, car, bus and motorcycle instead of only one or all? thanks a lot for sharing ur knowledge!

Hello, I have a problem with LineZone in supervision 0.7.0, and it not working. I've tried to follow the same way with your previous video about track and count object, any idea?

Can it be used to count humans on real if yes can u tell the possible changes

Hi dude, your video is very good. I have a question. I trained a four-label model. I want to show the count of each label in this model separately on the screen. Do you have a suggestion?

I don't have NVidia cards (nor can I use CUDA for that matter). How can I make use of the GPU when running the "yolo detect predict..." local inference on processors with UHD Graphics 600 & 630 and Intel N100 & N200 processors?

latest changes : while True: ret, frame = cap.read() if not ret: cap.set(cv2.CAP_PROP_POS_FRAMES, 0) continue result = model(frame)[0] detections = sv.Detections.from_ultralytics(result) labels = [f"{model.model.names[class_id]} confidence:{confidence}" for _, _, confidence, class_id, _ in detections]

thank you i have a question i have a trained instance segmentation yolov8 model and also detectron2 model on custom dataset and what i need is to run inference on new data images and use the output to make annotation on the new images and add them to my train dataset by uploading them to my roboflow dataset later so is there any way i can do that ?

What would be the best way to have multiple conditions for the detections so, for example: [detections.class_id !=0 && detections.confidence >= 0.7] ?

Did they remove yolov8 compatibility from supervision? Mine insists there isn't v8 version only v5

Hello, thank you for your great video! May i ask you a question, can it be use on yolov7 model?

Hey @Roboflow can this same model work in Raspberry pi 5 or Nvidia Jetson Nano without any optimization or quantization ?

Hi Friend, great tutorial! Cheers for that.. how can I know the class number of a given item? for example, you know the class number of apple and person.. You got it somewhere probably :) where can i see this list so i can filter other items from the list? thanks!

Hi! can I use Yolov8 via a live stream link, not via a connected webcam, only via the player link?

i wanna clear all my installed dataset and args to make another yolo. how can i do that???? Its a long time problem. PLEASE HEEELP TO CLEAR ALL before making another version!

I don't know if anyone has requested but, would it be possible for you to do a video using YoloV8 ONNX Object Detection Counting in Real-time with OpenVINO ? That would be one really interesting video to watch!! :)

Is there a way to get the coordinates of the bounding box in real time from Supervision or YOLOV8 itself?

bonjour merci pour le travail ! tu utilise la version 0.2.0 de supervision mais il n'y a que la versin 0.16.0 de dispo qui ne contient pas la fonction detections.from_yolov8 ! comment puis je faire ? merci

I know yolo5 and 7 supports multiple streams, does Supervision support multiple streams?

Congratulations on the job. Can you make a video of yolov8, mss and numpay, capturing the image directly from the monitor screen?

It looks awesome. May I use your script, then where can I get it?

Thank you for this great tutorial! May be you can help me with this error I am getting? When I run the main with the "results = model(frame)" line added, it is throwing the following error (see below). If I use YOLOv5, it works perfect, but with YOLOv8 it throws this error. I have created a virtual environment and followed the tutorial step by step. Any ideas? Thanks! OSError Traceback (most recent call last) Cell In [16], line 22 19 break; 21 if __name__ == "__main__": ---> 22 main(model) Cell In [16], line 10, in main(model) 7 ret, frame = cap.read() 8 assert ret ---> 10 result = model(frame) 12 cv2.imshow("yolov8", frame) 14 k = cv2.waitKey(1) File c:\Python38\lib\site-packages\ultralytics\yolo\engine\model.py:58, in YOLO.__call__(self, source, **kwargs) 57 def __call__(self, source, **kwargs): ---> 58 return self.predict(source, **kwargs) File c:\Python38\lib\site-packages\torch\autograd\grad_mode.py:27, in _DecoratorContextManager.__call__..decorate_context(*args, **kwargs) 24 @functools.wraps(func) 25 def decorate_context(*args, **kwargs): 26 with self.clone(): ---> 27 return func(*args, **kwargs) File c:\Python38\lib\site-packages\ultralytics\yolo\engine\model.py:130, in YOLO.predict(self, source, **kwargs) ... --> 205 s = self._ext_to_normal(_getfinalpathname(s)) 206 except FileNotFoundError: 207 previous_s = s OSError: [WinError 123] The filename, directory name, or volume label syntax is incorrect: '[[[ 69 76 103] [ 67 75 102] [ 65 75 103] ... [ 56 71 96] [ 59 73 98] [ 60 73 99]] [[ 70 77 103] [ 70 77 104] [ 67 76 103] ... [ 59 73 98] [ 59 73 98] [ 60 73 99]] [[ 72 80 104] [ 71 78 103] [ 70 78 105] ... [ 62 74 98] [ 62 74 98] [ 61 73 98]] ... [[ 59 95 138] [ 61 97 139] [ 61 97 137] ... [ 35 48 49] [ 33 48 50] [ 33 50 51]] [[ 59 97 138] [ 59 97 138] [ 61 99 138] ... [ 37 49 51] [ 37 50 52] [ 36 51 53]] [[ 60 99 138] [ 60 99 138] [ 59 99 137] ... [ 40 50 52] [ 39 51 53] [ 38 51 53]]]'

I've been trying to run this code but its failing in the supervision/detection/core.py file... xyxy=yolov8_results.boxes.xyxy.cpu().numpy(), AttributeError: 'list' object has no attribute 'boxes' I put in a bug report.... Any idea what this might be? I'd love to be able to finish your suite of tutorials on this! I am using supervision 0.2.0 and I tried it with the latest 0.2.1.. same thing.... Thanks!

How can I use another webcam like on jetson nano , or custom

IndexError: index 738 is out of bounds for axis 0 with size 720

can we detect object if the object inside the object, example we only detect spoon if the spoon inside the cup bounding box

Hey, can you please guide me on overlapping object detection, as im willing to use YOLO for peach🍑 fruit detection in my project, the problem is, fruits are very dense and are overlapping with each other as well as occluded by the leaves, so can you please help me with that?

Hello Amin! I get the error: AttributeError: type object 'Detections' has no attribute 'from_yolov8''

Great video, unfortunately, several times, you have typed the code exactly where the youtube progress bar is, so when I wanted to follow you, I had to look for a better shot when the code was scrolled. Please type the code a little higher on the screen (if possible) next time. Thanks

Hi can you make a video on explaining the code of YoloV8 a little bit

how to count object in image by yolo8?

I have a problem with the resolution, I can't change it, I tried everything I know but still shows 640,480,3. Any solution?

Can you make a video to run with onnx model? I really appreciate that

Hi! I really love your tutorial, but I have one question... If I want to recognize other object, that is not in the data? ex: recognize wheels, what should I do? Thanks!!!

Can I build the program on Windows OS as you did on Linux?

Is there a way to pass yolov8 a numpyArray instead of cv2.VideoCapture?

i got big error. the msg is AttributeError: type object 'Detections' has no attribute 'from_yolov8'. Did you mean: 'from_yolov5'?

How do I quit running the feed without killing my terminal? Dumb question but im very new to this

thanks but it wont work for mac os 10.15

i get this error AttributeError: type object 'Detections' has no attribute 'from_yolov8' how do i fix them * i install using conda env (pip instal --user supervision) supervison (0.16.0) code : import os import cv2 import supervision as sv from supervision import Detections from ultralytics import YOLO hand_sign = {0: "good_luck", 1: "hello", 2: "ily", 3: "no", 4: "please", 5: "sorry", 6: "thank_you", 7: "yes"} model_path = os.path.join('.', 'runs', 'detect', 'train', 'weights', 'last.pt') model = YOLO(model_path) cap = cv2.VideoCapture(0) box_annotate = sv.BoxAnnotator( thickness=2, text_thickness=2, text_scale=1 ) while True: ret, frame = cap.read() result = model(frame)[0] detect = sv.Detections.from_yolov8(result) # labels = [] frame = box_annotate.annotate(scene=frame, detections=Detections) cv2.imshow("yoloV8", frame) if(cv2.waitKey(1) == 27): break

how i can use my trained yolov8 model ?

Bro how to store the object count value in the variable?

I don't know how to deploy this in web framework with tracking and counting

while True: ret, frame = cap.read() result = model(frame)[0] detections = sv.Detections.from_yolov5(result) ****.from_yolov8 is not available attrribute? so i had to change to .from_yolov5 labels=[ f"{model.model.names[class_id]},{confidence:0.2f}" for _ , confidence, class_id, _ in detections ]

Hello can we count number of objects when one of them enters the zone, just using supervision libary?

How can I write the same code in .NET Core C#? 🙂

How can i use the tutorial with pycharm

hey bro please help me if want to make the output label in another language how can i do that in mainpy file i want to make if yolo detect class 0 show label in another language 🙏

Are you for hire to help me with a project?

Let's all wait, shall we? Soon someone shares the knowledge on youtube. Then we can adapt the code. I even think I'll make a video on youtube too. 🙂

hello, firstly thank you but i have this error I don't no why for _, confidence, class_id, _ ^^^^^^^^^^^^^^^^^^^^^^^^^^ ValueError: too many values to unpack (expected 4)

Yolov8 is still in early days. Buggy on compatibility, backward-compatible is a no go, export is buggy etc... Stay with Yolov5.

How to turn off the webcam 1:45

For the labels ValueError: too many values to unpack