YOLOv3 | YOLOv3 based on TensorFlow | Machine Learning library

I am trying to copy a file into my docker container but the command fails. The file is in the same directory as the Dockerfile, so I don't understand the reason for the error.

I'd appreciate any help or advice. Thanks beforehand.

This is the code:

I was learning, working of object detection models Faster R CNN,YOLOv3 and SSD.I got confusion with anchor box size refining.

I am trying to run a object detection code in Aws. Although opencv is listed in the requirement file, i have the error "no module named cv2". I am not sure how to fix this error. could someone help me please.

My requirement.txt file has

• opencv-python
• numpy>=1.18.2
• scipy>=1.4.1
• wget>=3.2
• tensorflow==2.3.1
• tensorflow-gpu==2.3.1
• tqdm==4.43.0
• pandas
• boto3
• awscli
• urllib3
• mss

I tried installing "imgaug" and "opencv-python headless" as well.. but still not able to get rid of this error.

I have finished an object detection project and I want to make a demo of it with a web application. I use YOLOv3 on Darknet for training. I intend to get the weights that I got from the training and use it in the web app. I decided to allow the user to upload an image, then take that image through a Python script, which will load my trained weights and do the detection. I decided to use PHP for the server-side and test everything on the localhost first.

I am totally new to web application and I don't know if I decided right

I'm new to yolo and currently using YOLOv3. I don't have a expensive GPU and like to test with YOLOv3 to detect only vehicles (8 classes). Is there a way I can do this without re-training the model by changing some configs? I'm using the weights file YOLOv3-608.

I'm currently trying to implement YOLOv3 object detection model in C(only detection, not training).

I have tested my convolution method with arbitrary values and it seems to be working as I expected.

Before stacking up multiple method calls to do forward propagation, I thought it would be safe to test with the actual pretrained weight file data.

When I look up Darknet's pre-trained weight file, it was a huge chunk of binary files. I tried to convert it to hex and decimals, but it still doesn't look simple to pinpoint what part of values to use.

So, my question is, what should I do to extract the decimal numbers of the weights or the filter values so that I can use them in the same order of the forward propagation happening in YOLOv3?

*I'm currently trying to build my c version of YOLOv3 using the structure image shown in https://www.itread01.com/content/1541167345.html

*My c code will be run on an FPGA board called MicroZed, along with other HDL code.

*I tried to plug some printf functions into some places of Darknet code to see what kinds of data are moving around when YOLOv3 runs, however, when I ran it on in Linux terminal, it didn't show anything new and kept outputting the same results.

Any help or advice will be really appreciated. Thank you!

Steps to reproduce:

I am using Anaconda on Windows to set up environment for this repo.

conda create --name pytorch-yolo

Then I install all dependencies with conda install --file requirements.txt

Which returns

I am trying to run a code using PysimpleGUIqt but it gives following error:

I have tried to install windows but getting following error:

If I try to install window it will be installed but error remains. I even installed pyqt5 but nothing changed. Have no idea how to solve it. The whole code is attached below for your reference which is originally taken from here:

I have a question about splitting a dataset of 20k images along with their labels, the dataset is in the format of YOLOv3 which has an image file and a .txt file with the same name as the image, the text file has the labels inside it.

I want to split the dataset into train/test splits, is there a way to randomly select the image and its labels .txt file with it and store it in a separate folder using Python?

I want to be able to split the dataset randomly. For instance, select 16k files along with label file too and store them separately in a train folder and the remaining 4k should be stored in a test folder.

This could manually be done in the file explorer by selecting the first 16k files and move them to a different folder but the split won't be random as I plan to do this over and over again for the same dataset.

Here is what the data looks like images and labels screenshot