Deep Learning Car Detection using satellite imagery on Google Colab || YOLO || GeoDev

Object detection is a task in computer vision that involves identifying the presence, location, and type of one or more objects in a given photograph.

It is a challenging problem that involves building upon methods for object recognition (e.g. where are they), object localization (e.g. what are their extent), and object classification (e.g. what are they).

In recent years, deep learning techniques are achieving state-of-the-art results for object detection, such as on standard benchmark datasets and in computer vision competitions. Notable is the “You Only Look Once,” or YOLO, a family of Convolutional Neural Networks that achieve near state-of-the-art results with a single end-to-end model that can perform object detection in real-time.

Colab Link: https://colab.research.google.com/dri...
Darknet GitHub link: https://github.com/AlexeyAB/darknet
Dataset: https://www.kaggle.com/datasets/tekba...

process.py file:

import glob, os
Current directory
current_dir = os.path.dirname(os.path.abspath(__file__))
print(current_dir)
current_dir = 'data/data/train'
Percentage of images to be used for the test set
percentage_test = 10;
Create and/or truncate train.txt and test.txt
file_train = open('data/train.txt', 'w')
file_test = open('data/test.txt', 'w')
Populate train.txt and test.txt
counter = 1
index_test = round(100 / percentage_test)
for pathAndFilename in glob.iglob(os.path.join(current_dir, "*.jpg")):
title, ext = os.path.splitext(os.path.basename(pathAndFilename))
if counter == index_test:
counter = 1
file_test.write(current_dir + "/" + title + '.jpg' + "\n")
else:
file_train.write(current_dir + "/" + title + '.jpg' + "\n")
counter = counter + 1



Timestamp:
0:00 Introduction
5:09 About dataset
7:26 Annotate your own dataset
9:52 Starting with google colab
14:36 Getting started with yolov4-tiny
24:10 Training the model
27:49 Evaluating the model

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