The following error comes up: ValueError: too many values to unpack (expected 4) It is in the following lines: self.labels = [f"{self.CLASS_NAMES_DICT[class_id]} {confidence:0.2f}" for _, confidence, class_id, tracker_id in detections]
Try to make this change , it solved the problem for me Just add another '_' after 'for' self.labels = [f"{self.CLASS_NAMES_DICT[class_id]} {confidence:0.2f}" for _,_, confidence, class_id, tracker_id in detections]
Hello, thank for your video But i have a problem: from supervision.tools.detections import Detections, BoxAnnotator ModuleNotFoundError: No module named 'supervision.tools' Can you fix it? Thank you, have a nice day!
Great video. However I wonder how to get a very smoothly output. I have tried multithreading however the output is a bit of choppy. Not tottaly smooth. Do you have any solution on that
Hi, I would like to know how to count the amount of detected object per class. Is this possible? I could only get the amount of all the detected objects.
Thank you for the video. How I can set a filter to detect and draw BBox ONLY for "Person"? Is it possible to set up an area in the video frame the detector detects the objects only in that area?
Thanks a lot for watching! Yeah u can just crop out your region of interest. And if u check for index 0 in the classes it corresponds to a person. I have another video here on the channel where I implement Yolov8 in a class for person detection
@@NicolaiAI Nevermind, got it fixed! Keep up the great work though! Your course on YOLO v8 must be really in depth if you can afford to teach us this for free!
Thank you, you helped me a lot, but I still have a problem. I trained a model to recognize license plates. It identified them, but I also want it to print the number of the car that it identified. Can you explain this method?
I want to transfer the names of the detection objects in yolov8's real time analysis to the mobile application in real time via websocket, how do I do that?
I came here after seeing your comment link. I'm trying to load my trained best.onnx file. However, I'm getting the following error: return self._sess.run(output_names, input_feed, run_options) onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: images for the following indices index: 2 Got: 640 Expected: 320 index: 3 Got: 640 Expected: 320 Please fix either the inputs or the model. I tried adding self.img_size = (320, 320) in __init__ and modifying the predict method as follows: def predict(self, frame): frame = cv2.resize(frame, self.img_size) results = self.model(frame) return results But I still encounter the same error. Is there any specific reason for this?
U are trying to use onnx with PyTorch. U can’t really do that since onnx is a whole another framework. In my yolov7 course I go over how to use custom trained yolo models with onnx framework
Try to make this change , it solved the problem for me Just add another '_' after 'for' self.labels = [f"{self.CLASS_NAMES_DICT[class_id]} {confidence:0.2f}" for _,_, confidence, class_id, tracker_id in detections]
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where is the code can you share me the git link of your repo
pls
The following error comes up:
ValueError: too many values to unpack (expected 4)
It is in the following lines:
self.labels = [f"{self.CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
for _, confidence, class_id, tracker_id
in detections]
hi did it get solved or not. Because it also gives me same value error . please help if you solve this error
Same error
Try to make this change , it solved the problem for me
Just add another '_' after 'for'
self.labels = [f"{self.CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
for _,_, confidence, class_id, tracker_id
in detections]
Hello, thank for your video
But i have a problem:
from supervision.tools.detections import Detections, BoxAnnotator
ModuleNotFoundError: No module named 'supervision.tools'
Can you fix it? Thank you, have a nice day!
Hi Thanks for watching! Did u pip install supervision?
@@NicolaiAI Yupp, i installed it, But it still error
@@NicolaiAI In my supervision library there is no "tool" module but there is still "draw" module, I don't understand why
same problem
@@adrianknutsen i fixed it, please remove "supervision" version 0.2.0 and install "supervision" version 0.10
speak slowly ... be indian
Thanks for that Video, it's really helpful.
if possible, can you share the name of your chair plz😅.
Thanks a lot for watching! My chair in the background is from IKEA and my Office chair is a Herman Miller embody
Thank you, it’s so clear and detail.
Glad you like it! Thanks for watching
Great video. However I wonder how to get a very smoothly output. I have tried multithreading however the output is a bit of choppy. Not tottaly smooth. Do you have any solution on that
Thanks! Are u thinking about tracking? I have a course about that with yolov8
Hi, I would like to know how to count the amount of detected object per class. Is this possible? I could only get the amount of all the detected objects.
U could have a list with all the counters and then increment the index in that list based on the label’s index
Thank you for the video. How I can set a filter to detect and draw BBox ONLY for "Person"? Is it possible to set up an area in the video frame the detector detects the objects only in that area?
Thanks a lot for watching! Yeah u can just crop out your region of interest. And if u check for index 0 in the classes it corresponds to a person. I have another video here on the channel where I implement Yolov8 in a class for person detection
Great Video! But I unfortunately had an error occurred, "TypeError: unsupported format string passed to list.__format__". Any idea on how to fix this?
In what line do u get the error?
@@NicolaiAI Nevermind, got it fixed! Keep up the great work though! Your course on YOLO v8 must be really in depth if you can afford to teach us this for free!
@@mystuff6071 there is also some theory in the course and how to do everything from scratch. Also different ways to deploy the model
If I only want to display bboxes with confidence >0.5 where Can I do this in the code? Great video!!
U Can basically just have an if statment checking the confidence score and then only visualize or store if greater than threshold
@@NicolaiAI Hey, I couldn't implement said statement into your code. Is there any way you can help?
@@NicolaiAI In which method should this if statement be made please? Cannot understand a way where to put this statement.
Thank you, you helped me a lot, but I still have a problem. I trained a model to recognize license plates. It identified them, but I also want it to print the number of the car that it identified. Can you explain this method?
I want to transfer the names of the detection objects in yolov8's real time analysis to the mobile application in real time via websocket, how do I do that?
Can YOLOv8 perform instance segmentation in real-time?
Yeah for sure! I actually have a video about that aswell. Even for a custom dataset
@@NicolaiAI Wow... I found it. That's brilliant!!!
th-cam.com/video/4iRFGjNtzNo/w-d-xo.html
@@JasonKim-yz8rv great to hear! Hope u can use it
Can this implement in raspberry pi? Will this makes the fps drops?
it says there is no modele named supervision help!
Did u pip install it?
@@NicolaiAI thankss
After pip installing. It says there us no module named supervision.tools
@@yaseenpk1739 Did you figure this out? I'm getting the same error.
Congratulations on the video. Could you make a video like this, but using monitor screen?
Hi, I have the next error: ColorPalette.__init__() missing 1 required positional argument: 'colors'. Any idea?
Did u know how to fix this error
Has this error. Use 'sv.ColorPalette.default()' instead.
I came here after seeing your comment link.
I'm trying to load my trained best.onnx file. However, I'm getting the following error:
return self._sess.run(output_names, input_feed, run_options)
onnxruntime.capi.onnxruntime_pybind11_state.InvalidArgument: [ONNXRuntimeError] : 2 : INVALID_ARGUMENT : Got invalid dimensions for input: images for the following indices
index: 2 Got: 640 Expected: 320
index: 3 Got: 640 Expected: 320
Please fix either the inputs or the model.
I tried adding self.img_size = (320, 320) in __init__ and modifying the predict method as follows:
def predict(self, frame):
frame = cv2.resize(frame, self.img_size)
results = self.model(frame)
return results
But I still encounter the same error. Is there any specific reason for this?
U are trying to use onnx with PyTorch. U can’t really do that since onnx is a whole another framework. In my yolov7 course I go over how to use custom trained yolo models with onnx framework
@@NicolaiAI th-cam.com/video/UNVIZBK45kk/w-d-xo.html
Is the video in question correct?
Thank you for your prompt response!
Yeah that’s the course I’m talking about. Then u can do inference with ONNX
How can we print the objecct detected?
Thank you so much, this is really helpful and well explained.
Glad it was helpful! Thanks for watching
why do you have two cameras.
Some people might find it interesting to see how it is behind the scenes
Hi, I got ValueError: too many values to unpack (expected 4) on line 68
Hi, did you solve this problem?
Same error
Try to make this change , it solved the problem for me
Just add another '_' after 'for'
self.labels = [f"{self.CLASS_NAMES_DICT[class_id]} {confidence:0.2f}"
for _,_, confidence, class_id, tracker_id
in detections]