Відео

Great teaching! Appreciate but would prefer no background music as it serves no purpose, it is harder to listen. Wish you would understand this. Thank you!

I love how happy you are! Thanks for sharing

Work from home jobs available?? I am interested

Excelent video! how can I load a image that not exists in the dataset and make a prediction?

Must one have installed fiftyone package?

Tell me, can I use any lidar? Can livox Horizon be used for detection?

Thank you!

Finally we might get rid of NMS 😂

Wow❤

which is better?

Terrible, lazy video. Waste of time. This tells me any of the things any ML engineer should be interested in. Nothing about the model architecture, the training data, Hugging Face api, any technical detail, any performance benchmarks, nothing about re-training or transfer learing.. You just showed unreadable code snippets and waved your hands. What is the point? Useless.

Hey! I'm training in my computer and after one epoch I receive this message: The Kernel crashed while executing code in the current cell or a previous cell. Please review the code in the cell(s) to identify a possible cause of the failure. Click here for more info. View Jupyter log for further details.

does this works on languages other than english?

Is there is any video while building it?

Can you provide the Code?

Thank you! It's very simple explanation so I can understand it better

Parking space available app. Camera identifies open spots and provides "green shading" in real time to Parking app. Consider a large airport (tens of cameras providing GPS coordinates to GPS enabled phone). Same app could count cars providing subscription services on traffic volume (even parse by vehicle type by day to show more affluent customers to businesses catering to such clientele).

Please, can you help me work lidar in cvat, is it possible?

in some of your jupyter notebook saw the code from clearml import Task from ultralytics import YOLO # Step 1: Creating a ClearML Task task = Task.init(project_name="my_project", task_name="my_yolov8_task") # Step 2: Selecting the YOLOv8 Model model_variant = "yolov8n" task.set_parameter("model_variant", model_variant) # Step 3: Loading the YOLOv8 Model model = YOLO(f"{model_variant}.pt"), tell me where this file I want to continue to perform steps on tutorial

Nice video. I would like to see more 3D content. Please, go on.

my google colab is not loading content in the file u provided

Nice.

it seems streaming on gradio isn't working

which is current SOTA Image Classification Technique?

Link in bio cannt be copied or clicked from the UA-cam app, so we will never see the full video :/

Thank you for sharing this clear and simple information. Very easy to understand.

Tq

Great video sir!! Can you make a video demonstrating how to read text from a detecting box and then use the text in a database? Also the playlist on ADAS is really good, learning a lot about LiDARs

Great Video, thank you

Repository link

I have a question. When aı try to export it, it is always in process. When is it gonna end?

If i click a create space it. Shows you have been rate-limted you can retry action later like that ....what can i do

On a side note. Can i use yolov8 for realtime custom object detection without using the ultralytics library?

Hi sir, I have a segmentation annotation task, wherein I have 30 classes. The cvat application doesnt seem to be assigning a label value above 9. Is there some restriction in the number of classes we can annotate. How do i tackle this problem?

i run the code on 7 epochs and it always give me MaP : 0.00000e+00

when i use the code and train the model on 7 epochs , the MaP is always 0.00000e+00 , please give me a sulotion

Thank you so much for uploading this video on UA-cam, if possible can you please make a video on how to use openPose along with open CV in a unity environment.

THANKS for this video, can you tell me how you get around the "catastrophic forgetting" notice when adding new classes and training data to an existing model?

So where are the OpenCV "tutorials" to get started on this topic?

Mediapipe does support multiperson detection now

Great to have an explanation at a much slower speed. Most machine learning tutorials go so fast I can't absorb the information. Also a much a more smoother transition between general description and advanced concepts. Not just throwing in advanced concepts suddenly, so the viewer has to stop playback and start looking things up elsewhere to keep up. I particularly like the way the narrator goes back and checks the viewer has picked up on a concept.

very nicely explained

I like this video! It answered a lot of questions I had as a beginner. Thank you so much! One question. This video is mainly about bounding box annotation. What about with key-point annotation? I am going to annotate mice in a cage, which means the objects are highly occluded. But I would like to use key-point annotation to detect their behaviour. What would be the best way to annotate to be consistent do you think?

i thought it was in real time....

Wow! Beginner want such class to understand. We want more of such tutorials.

Excellent tutorial! Thanks a lot...

how to train datasets using Yolov8 and SAHI?

Thank you for posting this great tutorial. I was looking for it for quite a while. I am looking for a board to place inside my car. I want to use realtime object detection to recognise whenever a certain type of car is passing by in front of my car. And I want the board to notify me by 4g/lte sms( no wifi connection so i have to add a gsm/lte/4g module I am looking at 2 boards: 1. Raspberry pi 5 8Gb with Google Coral USB Accelerator 2. Google Coral Dev Board 4Gb Which one is the best option?

Where are the visualized images stored? Where exactly in the memory? Where i can see them?