Yolov5 | Efficient implementation of YOLOV5 in TensorFlow2 | Computer Vision library

im trying to bind the Object Tracking with Deep Sort in my Project and i need to get the boxes, scores, classes, nums.

Loading Pretrained Yolov5 model:

I'm trying to take the output of a yolov5s.onnx model and and run NMSBoxes on it. But I keep getting this error:

I'm trying to make a currency recognition model and I did so using a dataset on kaggle and colab using yolov5 and I exactly carried out the steps explained on yolov5 github. At the end, I downloaded a .pt file which has the weights of the model and now I want to use it in python file to detect and recognize currency . How to do this?

I am a beginner in computer vision and I am totally confused about what to do. I am searching over and over but I don't reach anything.

I’m currently working on object detection using yolov5. I trained a model with a custom dataset which has 3 classes = [‘Car’,‘Motorcycle’,‘Person’]

I have many questions related to yolov5.

All the custom images are labelled using Roboflow.

question1 : As you can see from the table that my dataset has mix of images with different sizes. Will this be a problem in training? And also assume that i’ve trained the model and got ‘best.pt’. Will that model work efficiently in any dimensions of images/videos.

question 2:

Is this directory model correct for training. Even i have ‘test’ directory but it seems that the directory is not at all used. The images in the ‘test’ folder is useless. ( I know that i’m asking dumb questions, please bare with me.)

Is it ok if place all my images like this

And should i need a ‘test’ folder?

question3: What is the ‘imgsz’ in detect.py? Is it downsampling the input source?

I’ve spent more than 3 weeks in yolo. I love it but i find some parts difficult to grasp. kindly provide suggestion for this questions. Thanks in advance.

Please i need you help concerning my yolov5 training process for object detection!

I try to train my object detection model yolov5 for detecting small object ( scratch). For labelling my images i used roboflow, where i applied some data augmentation and some pre-processing that roboflow offers as a services. when i finish the pre-processing step and the data augmentation roboflow gives the choice for different output format, in my case it is yolov5 pytorch, and roboflow does everything for me splitting the data into training validation and test. Hence, Everything was set up as it should be for my data preparation and i got at the end the folder with data.yaml and the images with its labels, in data.yaml i put the path of my training and validation sets as i saw in the GitHub tutorial for yolov5. I followed the steps very carefully tought.

The problem is when the training start i get nan in the obj and box column as you can see in the picture bellow, that i don't know the reason why, can someone relate to that or give me any clue to find the solution please, it's my first project in computer vision.

This is what i get when the training process starts

This the last message error when the training finish

I think the problem comes maybe from here but i don't know how to fix it, i used the code of yolov5 team as it's in the tuto

The training continue without any problem but the map and precision remains 0 all the process !!

Ps : Here is the link of tuto i followed : https://github.com/ultralytics/yolov5/wiki/Train-Custom-Data

I am using a NN to detect 4 types of objects (chassis, front-spoiler, hubcap, wheel) in the live feed of my webcam. When one is detected, I want to display an image with information about it (chassis.png, front-spoiler.png, hubcap.png, wheel.png). When I run my NN and hold one of the items in front of the webcam, the opencv windows freezes and doesnt display anything. What is the reason for that?

I am using YOLOv5s for object detection on custom datasets, there are multiple objects in given video, sometimes label text and bounding box thickness looks very bad. how can I customize these things?

I was able to run the Flask app with yolov5 on a PC with an internet connection. I followed the steps mentioned in yolov5 docs and used this file: yolov5/utils/flask_rest_api/restapi.py,

But I need to achieve the same offline(On a particular PC). Now the issue is, when I am using the following:

How to deploy Custom trained YOLOV5 model to azure using azure functions? I couldn’t find any online resources

Complete Scenario:

There is a sharepoint app where user will upload the videos, once the new video is uploaded, it should trigger the flow to azure function, this azure function should be able to predict the objects in the frame with the custom trained yolov5 model