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Обнаружение объектов на изображении с помощью моделей YOLOv5 и YOLOv8

Автор статьи: Виктория Ляликова

Обнаружение объектов является достаточно популярной задачей компьютерного зрения, которая включает в себя идентификацию и обнаружение объектов на изображениях или видео. Данная задача является частью многих приложений, например, таких как беспилотные автомобили, робототехника, видеонаблюдение и т.д. За прошедшие годы разработано множество алгоритмов и методов для поиска объектов на изображениях и их положениях. Наилучшее качество выполнения таких задач достигается при использовании сверточных нейронных сетей.

Одной из самых популярных архитектур нейронных сетей для таких задач, является YOLO (you only look once), созданная в 2015 году. С тех пор появилось довольно много версий данных алгоритмов. Последние выпуски сети предназначены для таких задач как распознавание, обнаружение и сегментация изображений.

Мы будем рассматривать архитектуру YOLO только для задачи обнаружения объектов на изображении. В данном случае цель алгоритма - предсказать класс объекта и нарисовать ограничивающую рамку, которая определяет местоположение объекта на входном изображении.

Один из способов задач детекции состоит в разбиении изображения на квадратные области, а затем классификация этих областей на наличие объекта и классификацию самого объекта. Таким образом изображение просматривается дважды, один раз для определения областей где есть объект, а второй - для классификации этого объекта. YOLO использует другой подход. Исходное изображение сжимается таким образом, чтобы получить квадратную матрицу, в каждой клетке которого записана информация о наличии объекта и классе этого объекта на соответствующей части картинки. Для каждой ячейки выводится вероятности определяемого класса. Ячейки, имеющие вероятность класса выше порогового значения, выбираются и используются для определения местоположения объекта на изображении. То есть YOLO просматривает картинку один раз, что существенно увеличивает скорость обработки. Отличается высокой скоростью и точностью обнаружения объектов. На выходе работы такой сети мы хотим получить примерно такое изображение:

Таким образом, мы получили изображение с обозначением детектированных объектов и значением вероятности принадлежности к выбранному классу.

Ниже мы разберем примеры обнаружения объектов на моделях YOLOv5 и YOLOv8 в двух вариантах.

Предположим, что мы имеем одно или несколько изображений и наша задача состоит в обнаружении на нем максимального количества объектов, которое позволит модель.
Предположим, что у нас есть набор данных и мы хотим обучить модель YOLO именно на этом наборе.

Обе версии YOLO бесплатные от одного разработчика Ultralytics, хорошо показывают себя сравнительных тестах и реализованы на современной библиотеке глубокого обучения PyTorch. Модель YOLOv8 позиционируется как более новая и современная по сравнению с 5й версией, хотя обе версии активно развиваются.

Начало работы с YOLO и подготовка датасета

Модели YOLO поставляются уже предварительно обученными на наборе данных COCO и экспортированы в файлы с расширением .pt. Существует три типа моделей (классификация, обнаружение, сегментация) и по 5 моделей разного размера для каждого типа:

Классификация	Обнаружение	Сегментация	Модель
yolov8n-cls.pt	yolov8n.pt	yolov8n-seg.pt	Nano
yolov8s-cls.pt	yolov8s.pt	yolov8s-seg.pt	Small
yolov8m-cls.pt	yolov8m.pt	yolov8m-seg.pt	Medium
yolov8l-cls.pt	yolov8l.pt	yolov8l-seg.pt	Large
yolov8x-cls.pt	yolov8x.pt	yolov8x-seg.pt	Huge

Набор данных COCO представляет собой огромную коллекцию изображений 80 классов. Поэтому, если нет особых потребностей, то можно просто запустить уже готовую модель без предварительного обучения и оценивать результат. Посмотрим на названия классов набора COCO.

names: { 0: person, 1: bicycle, 2: car, 3: motorcycle, 4: airplane, 5: bus, 6: train, 7: truck, 8: boat, 9: traffic light, 10: fire hydrant, 11: stop sign, 12: parking meter, 13: bench, 14: bird, 15: cat, 16: dog, 17: horse, 18: sheep, 19: cow, 20: elephant, 21: bear, 22: zebra, 23: giraffe, 24: backpack, 25: umbrella, 26: handbag, 27: tie, 28: suitcase, 29: frisbee, 30: skis, 31: snowboard, 32: sports ball, 33: kite, 34: baseball bat, 35: baseball glove,36: skateboard, 37: surfboard, 38: tennis racket, 39: bottle, 40: wine glass, 41: cup, 42: fork, 43: knife, 44: spoon, 45: bowl, 46: banana, 47: apple, 48: sandwich, 49: orange, 50: broccoli, 51: carrot, 52: hot dog, 53: pizza, 54: donut, 55: cake, 56: chair, 57: couch, 58: potted plant, 59: bed, 60: dining table, 61: toilet, 62: tv, 63: laptop, 64: mouse, 65: remote, 66: keyboard, 67: cell phone, 68: microwave, 69: oven, 70: toaster, 71: sink, 72: refrigerator, 73: book, 74: clock, 75: vase, 76: scissors, 77: teddy bear, 78: hair drier, 79: toothbrush}

Любое обучение нейронной сети начинается с подготовки данных для обучения. Поговорим немного о том, где можно взять данные для обучения и как их представить в том формате, какой необходим для обучения моделей сети YOLO.

Для обучения модели нам необходимо подготовить датасет изображений и размеченных на нем объектов, а потом разделить эти данные на наборы для обучения и проверки.

Структура набора данных для обучения моделей YOLO должна иметь такой формат

├── dataset
│   ├── test
│   │   ├── images
│   │   └── labels
│   ├── train
│   │   ├── images
│   │   └── labels
│   └── val
│       ├── images
│       └── labels
├── dataset.yaml
└── yolov5x.pt

Датасет должен быть разделен на 2 папки: train (тренировочная) и val (валидационная). Также может быть еще необязательная папка test (для тестирования модели). В каждой папке лежат еще 2 папки: images (изображения) и labels (метки) - папка с текстовыми файлами аннотаций, содержащими метки объектов на этих картинках в формате YOLO. Данный формат подразумевает, что каждая строка файла представляется в виде:

{object_class_id} {x_center} {y_center} {width} {height}

Все координаты должны быть нормализованы так, чтобы они помещались в диапазон от 0 до 1. Для их расчета используются формулы

x_center = (box_x_left+box_x_width/2)/image_width
y_center = (box_y_top+box_y_height/2)/image_height
width = box_width/image_width
height = box_height/image_height

где box - ограничивающая рамка для объекта того класса, для которого мы производим вычисления координат.

Если на изображении несколько объектов, тогда текстовый файл аннотаций может выглядеть так

1 0.589869281 0.490361446 0.326797386 0.527710843
0 0.323529412 0.585542169 0.189542484 0.351807229

Первая строка содержит ограничивающую рамку для одного объекта, вторая строка содержит ограничивающую рамку для второго объекта. Конечно, файл может содержать и большее количество строк, в зависимости от количества объектов на изображении.

Текстовые файлы аннотаций должны иметь те же имена, что и файлы изображений, и иметь расширение .txt.

Далее в нашем датасете еще обязательно должен быть файл с расширением .yaml, который содержит пути до тренировочной и валидационной выборок, а также количество классов и их метки.

train: ../train/images
val: ../val/images
test: ../test/images
nc: 2
names: ['cat','dog']

В первых двух строчках указываются пути к изображениям и проверочного набора данных. Затем строка nc указывает количество классов, существующих в этих наборах данных, а имена представляют собой имена классов в правильном порядке. Индексы этих элементов - это числа, которые использовались при аннотировании изображений, и эти индексы будут возвращены моделью при обнаружении объектов с использованием метода прогнозирования. Файл .yaml будет передаваться при обучении модели.

И вот он главный вопрос, как получить такой набор данных?

Взять готовый датасет в свободном доступе. Можно например, воспользоваться сайтом kaggle.com. Но для того, чтобы найти набор в правильном формате, придется потратить время.
Также большой выбор бесплатных наборов для компьютерного зрения можно найти здесь https://universe.roboflow.com/. Чем удобен данный сервис, что датасет можно загружать в разных форматах.

Недостаток - много повторов и некачественной разметки.

Собрать набор данных самостоятельно, который потом, к сожалению, придется размечать вручную. Или даже если есть готовый набор, но не аннотированный, разметить его придется. Хорошая новость, что для визуального аннотирования изображений для задач машинного обучения., есть множество различных и удобных сервисов. Например, можно просто ввести в поисковую систему что-то вроде «программное обеспечение для аннотирования изображений для машинного обучения», чтобы получить их список. Есть также множество онлайн-инструментов, которые могут выполнить всю эту работу. Разметку, конечно, необходимо проводить в зависимости от модели сети, какую мы будем использовать. Так как мы можем разметить рамками, точками, областями сегментации и так далее. Одним из замечательных онлайн-инструментов для этого является Roboflow Annotate, подробнее можно посмотреть здесь Используя этот сервис, нужно просто загрузить изображения, нарисовать на них ограничивающие рамки и установить класс для каждой ограничивающей рамки. Затем инструмент автоматически создаст файлы аннотаций, разделит наши данные на наборы данных для обучения и проверки, создаст файл дескриптора YAML, а затем можно экспортировать и загрузить аннотированные данные в виде ZIP-файла.

Обнаружение объектов

YOLOv8

Сначала установим пакет YOLOv8, для этого в Jupyter Notebook необходимо набрать команду

%pip install ultralytics

В пакете Ultralytics есть класс YOLO, который используется для создания нейросетевых моделей. Далее импортируем этот модуль

from ultralytics import YOLO

Теперь все готово для создания модели нейронной сети

model = YOLO('yolov8m.pt')

При первом запуске такого кода в текущую папку будет загружен файл yolov8m.pt с сервера Ultralytics, который содержит модель и предобученные веса и далее будет создан объект модели. Теперь можно обучать эту модель, обнаруживать объекты и экспортировать ее для использования в производстве.

Для всех этих задач есть удобные методы:

train — используется для обучения модели на вашем наборе данных изображений.
predict — используется для предсказания указанного изображения, например, обнаружить ограничивающие рамки всех объектов, которые модель смогла найти на этом изображении.
export — используется для экспорта этой модели из формата PyTorch по умолчанию в указанный. Данный режим предлагает универсальный набор возможностей для экспорта обученной модели в различные форматы, что позволяет развертывать ее на различных платформах и устройствах.

Теперь мы можем взять любое изображение и обнаружить там объекты, которые были заложены в модель YOLO на этапе обучения.

results = model.predict('boy_dog.jpg')

Метод predict принимает множество различных типов входных данных, включая путь к одному изображению, массив путей к изображениям, объект Image библиотеки PIL Python и другие.

result = results[0]

result содержит обнаруженные объекты и удобные свойства для работы с этими объектами. Самым важным является массив boxes, который содержит информацию об обнаруженных классах, вероятности, с которым были приняты эти классы, а также координаты ограничивающих прямоугольников для каждого класса на изображении.

result.boxes

Результат

cls - номер класса (один из 80 типов объектов с идентификатором от 0 до 79. Классы объектов COCO были определены выше).
conf - уровень доверия модели для этого класса.
xywh - координаты прямоугольника в формате xywh.
xywhn - координаты прямоугольника в формате xywh, нормализованные по исходному размеру.
xyxy - координаты прямоугольника в виде массива [x1,y1,x2,y2]
xyxyn - координаты прямоугольника, нормализованные по исходному размеру изображения

То есть на нашем изображении найдено 2 класса с номерами 0 и 16, что соответствует классу человек и собака.

Можем переписать код и получить информацию в более удобном виде

def print_box(box):
    class_id, cords, conf = box
    print("Object type:", class_id)
    print("Coordinates:", cords)
    print("Probability:", conf)
    print("---")

[
    print_box([
        result.names[box.cls[0].item()],
        [round(x) for x in box.xyxy[0].tolist()],
        round(box.conf[0].item(), 2)
    ]) for box in result.boxes
]

Результат:

Если мы хотим получить именно изображения с уже ограничивающими рамками, это можно сделать следующим образом

from PIL import Image
for i, r in enumerate(results):
	img_bgr = r.plot()
	im_rgb = Image.fromarray(img_bgr[...,::-1])
	r.show()
	r.save(filename = f"results{i}.jpg")

2. YOLOv5

Если мы работаем с моделью YOLOv5, тогда необходимо просто склонировать репозиторий YOLOV с github и также работать с сетью. В этом случае последовательность действий такая.

Устанавливаем каталог, куда будем клонировать репозиторий

%cd D:/yolov5

Клонируем репозиторий с github

!git clone https://github.com/ultralytics/yolov5.git

Импортируем библиотеку pytorch

import torch

Переходим в нашу папку и устанавливаем зависимости из requirements.txt

%cd D:/yolov5/yolov5
!pip install -r requirements.txt

Для задач обнаружения, обучения и экспорта модели используются следующие команды:

Для обнаружения объектов набираем команду

!python detect.py --weights yolov5x.pt --source img.jpg

Входное изображение может быть просто файлом, скриншотом, папкой, видео и т.д. Веса (weights) указываются в зависимости от модели с которой мы хотим работать (nano, small, medium, large, xlarge).

Для обучения модели набираем команду

!python train.py --img 640 --epochs 10 --data coco128.yaml --weights yolov5s.pt

Необходимо указать размер изображения, количество эпох для обучения, набор данных, предобученные веса.

Для экспорта модели набираем команду

!python export.py --weights yolov5s.pt --include torchscript

Необходимо указать модель, которую будем экспортировать и формат. Данная команда экспортирует модель в формат torchscript.Также еще используются форматы: pytorch, onnx, openvino, engine, coreml и другие.

Теперь перейдем к загрузке изображения и получению результатов. Используем команду detect.py, предполагая, что у нас уже установлена директория проекта.

!python detect.py --weights yolov5x.pt --source D:/yolov_test1.jpg

Аргументы командной строки следующие

--source: путь к входному файлу

--weights: путь к файлу с весами yolov5, который хотим использовать для обнаружения, в данном случае yolov5x.pt

--project: необязательный аргумент, с помощью него указывается папка для сохранения результатов, которая в свою очередь будет содержать файл exp.

Получаем следующий результат

Здесь указана информация о том,какие параметры сети использовались (веса, набор данных на каком обучалась сеть, количество слоев в сети и т.д.) и какие результаты обнаружения получены (сколько и каких объектов обнаружено). Все результаты сохраняются в папку ...runs\detect\exp.

Здесь даже не нужен никакой дополнительный код для отрисовки ограничивающих рамок или определения имени класса, за нас уже все сделано. Мы получили готовое изображение с обнаруженными классами. Если на вход подавать папку с изображениями, в результате также в директории runs\detect\exp получим результаты обнаружения.

Обучение

YOLOv8

До этого мы использовали модели, предварительно обученные на хорошо известных объектах, но на практике скорее всего понадобится решение для обнаружения конкретных объектов для конкретной бизнес задачи. Предположим у нас есть размеченный набор данных и мы хотим научить собственную модель обнаруживать объекты какого-то типа.

Я взяла набор данных с сайта universe.roboflow по обнаружению трех цветов воздушных шаров. Набор удобен тем, что его структура каталогов организована как раз для обучения моделей YOLO. В наборе имеются папки train, val и test, а в каждой из этих папок есть папки labels и images, также имеется файл data.yaml.

В моем случае файл .yaml выглядит следующим образом

train: ../train/images
val: ../val/images
test: ../test/images

nc: 3
names: ['blue','green','red']

Всего имеется 744 изображения для обучения и 71 для валидации. Набор данных есть, теперь необходимо его загрузить в модель. Для обучения будем использовать метод train:

model.train(data = 'data.yaml', epochs = 20)

Предполагаем, что модель сети уже загружена с помощью команды

model = YOLO('yolov8m.pt')

Файл .yaml является единственным обязательным параметром, но также стоит задать количество эпох для обучения (по умолчанию равно 100). Конечно, настройки обучения включают в себя и другие гиперпараметры и конфигурации, но на них мы не будем останавливаться.. Эти настройки влияют на производительность, скорость и точность модели. Ключевые параметры тренировки включают batch size, learning rate (скорость обучения), weight_decay (параметр регуляризации L2). Также, значительно повлиять на процесс обучения может выбор оптимизатора, функции потерь и состава набора обучающих данных может. Каждый цикл обучения состоит из этапа обучения и этапа поверки.

На этапе обучения метод train выполняет следующие действия.

Извлекает случайную партию изображений из набора обучающих данных (количество изображений указывается с помощью параметра batch size).
Пропускает эти изображения через модель и получает результирующие ограничивающие рамки всех обнаруженных объектов и их классов.
Передает результат в функцию потерь, которая используется для сравнения полученных результатов с правильным результатом из файлов аннотаций для этих изображений. Функция потерь вычисляет величину ошибки.
Результат функции потерь передается оптимизатору для корректировки весов модели на основе величины ошибки в правильном направлении, чтобы уменьшить ошибку в следующем цикле. По умолчанию используется оптимизатор SGD (Stochastic Gradient Descent), но можно попробовать другие, например Adam, чтобы увидеть разницу.

На этапе проверки метод train выполняет следующее:

Извлекает изображения из набора проверочных данных.
Пропускает их через модель и получает обнаруженные ограничивающие рамки для этих изображений.
Сравнивает полученный результат с истинными значениями для этих изображений из текстовых файлов аннотаций.
Вычисляет точность модели на основе разницы между фактическими и ожидаемыми результатами.

Ход и результат для каждой эпохи отображаются на экране.

….

Фаза обучения включает в себя расчет величины ошибки в функции потерь, поэтому наиболее ценными метриками здесь являются box_loss и cls_loss.

box_loss показывает количество ошибок в обнаруженных ограничивающих рамках.
cls_loss показывает количество ошибок в обнаруженных классах объектов.

Почему потери разбиваются на несколько показателей? Потому что модель могла правильно определить ограничивающую рамку вокруг объекта, но неправильно определить класс объекта в этой рамке. Наиболее ценным показателем качества является mAP50-95, который представляет собой метрику Mean Average Precision (mAP). Данная метрика показывает на сколько безошибочно нейронная сеть смогла обнаружить объекты на изображениях из валидационной выборки. Если модель обучается и совершенствуется, точность должна расти от эпохи к эпохе.

Если после последней эпохи мы не получили приемлемой точности, тогда можно увеличить количество эпох. Также можно попробовать настроить другие параметры сеть. Для каждого случая необходимо подбирать свое решение.

Помимо этих метрик, во время обучения сети на диск записывается много статистики. Когда начинается обучение, создается подпапка /runs/detect/train в текущей папке и после каждой эпохи в нее записываются разные файлы журналов. Стоит заглянуть в эту папку, в ней много интересной информации. Данная директория сохраняется в директории проекта, если дополнительно не была установлена какая-либо другая директория. Также обученная модель поле каждой эпохи записывается в файл /runs/detect/train/weights/last.pt, а модель с наивысшей точностью — в файл /runs/detect/train/weights/best.pt. Итак, после завершения обучения мы должны получить файл best.pt для его использования.

Содержимое подпапки /runs/detect/train.

Содержимое файла results.png

Теперь посмотрим на наши результаты после обучения YOLOv8. Не забываем сначала загрузить новую модель.

Этот результат получен после 20 эпох обучения. Обучение заняло около 6 часов на компьютере с процессором AMD Ryzen 7 3700X 8-Core Processor 3.59 GHz, 32 ГБ оперативной памяти. На первом изображении пока пропущен 1 голубой шар и в 1 красном шаре модель немного сомневается, так как не очень высокая вероятность доверия, всего лишь 58%. Можно продолжить обучение или попробовать изменить параметры модели.

YOLOv5

Обучение производится с помощью команды train.py со следующими аргументами:

--data: путь к датасету для обучения

--img: размер изображения, подаваемого на вход yolo

--epochs: количество эпох

--weights: стартовые веса для обучения

--batch: размер батча, то есть количество картинок, одновременно подаваемых на вход yolo

Существуют другие настройки про которые можно почитать на странице проекта.

В данном случае я взяла набор данных с сайта universe.roboflow.com по доступности городской среды на улицах. Имеется 5 классов, на которые можно разделить препятствия, встречающиеся на улицах: затрудняет проезд (dificulta el paso), недоступно из-за строительства (inaccesible por obras), нужен пандус (necesita rampa), недоступный пандус (rampa inaccesible), неустойчивая площадка (suelo inestable).

Примеры изображений

Файл .yaml выглядит следующим образом

train: ../train/images
val: ../valid/images
test: ../test/images

nc: 5
names: ['dificulta el paso', 'inaccesible por obras', 'necesita rampa', 'rampa inaccesible', 'suelo inestable']

Всего имеется 7126 изображений для обучения и 918 для валидации. Попробуем запустить обучение на 10 эпох просто для примера. Конечно, для обучения модели на таком наборе данных, необходимо большее количество эпох.

!python train.py --img 416 --batch 16 --epochs 10 --data C:/yolov5/data1/data.yaml --weights C:yolov5/data1/yolov5x.pt

После 10 эпох результаты конечно еще не являются удовлетворяющими, но у меня не было цели добиться высоких показателей обнаружения.

Обучение проходит таким же образом, как и было описано выше при работе с моделью YOLOv8. Все результаты обучения также сохраняются в директорию runs/train/exp. При этом для каждого нового обучения создаются папки …./exp1, …/exp2 и т.д. .

Содержимое подпапки runs/detect/train

Файл results.png выглядит следующим образом

А теперь посмотрим на результат обнаружения, только теперь с параметром –weights мы загружаем нашу модель best.py, которая была получена после обучения. Должна быть установлена директория откуда запускать скрипт detect.py.

!python detect.py --weights runs/train/exp8/weights/best.pt --source C:/streets1.jpg

Я бы сказала, что с данными изображениями наша модель хорошо справилась, даже не смотря только на 10 эпох обучения.

Заключение

Целью статьи было познакомить читателей, которые только начинают работу в области компьютерного зрения и детекции объектов, с работой современных моделей нейронных сетей YOLOv5 и YOLOv8. То есть я не ставила цель решить какую-то сложную задачу или получить новые результаты, сравнить модели и так далее. Таким образом, мы рассмотрели задачу обнаружения объектов на предварительно обученных моделях YOLOv5 и YOLOv8, а также попробовали обучить модели для обнаружения объектов на своем датасете, обсудили вопрос создания своего датасета. При обучении таких моделей главное - это терпение, так как процесс обучения не быстрый, если вы не являетесь обладателем графического процессора и не арендуете облачный сервис с GPU).

В завершение приглашаем всех желающих на открытые уроки по компьютерному зрению в Otus:

10 июля: Семейство детекторов YOLO: от v1 до v10. Рассмотрим детекцию объектов, проследим эволюцию детекторов семейства YOLO от самой первой до самой актуальной версии (v10). Запись по ссылке
23 июля: Human pose estimation - обзор подходов, моделей и решений. Вы узнаете: постановку задачи оценки позы человека, обзор основных направлений к решению этой задачи и какие есть популярные фреймворки. Запись по ссылке

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9 июл. 2024 г.

07.09.23 16:24 CherryTeam

Cherry Team atlyginimų skaičiavimo programa yra labai naudingas įrankis įmonėms, kai reikia efektyviai valdyti ir skaičiuoti darbuotojų atlyginimus. Ši programinė įranga, turinti išsamias funkcijas ir patogią naudotojo sąsają, suteikia daug privalumų, kurie padeda supaprastinti darbo užmokesčio skaičiavimo procesus ir pagerinti finansų valdymą. Štai keletas pagrindinių priežasčių, kodėl Cherry Team atlyginimų skaičiavimo programa yra naudinga įmonėms: Automatizuoti ir tikslūs skaičiavimai: Atlyginimų skaičiavimai rankiniu būdu gali būti klaidingi ir reikalauti daug laiko. Programinė įranga Cherry Team automatizuoja visą atlyginimų skaičiavimo procesą, todėl nebereikia atlikti skaičiavimų rankiniu būdu ir sumažėja klaidų rizika. Tiksliai apskaičiuodama atlyginimus, įskaitant tokius veiksnius, kaip pagrindinis atlyginimas, viršvalandžiai, premijos, išskaitos ir mokesčiai, programa užtikrina tikslius ir be klaidų darbo užmokesčio skaičiavimo rezultatus. Sutaupoma laiko ir išlaidų: Darbo užmokesčio valdymas gali būti daug darbo jėgos reikalaujanti užduotis, reikalaujanti daug laiko ir išteklių. Programa Cherry Team supaprastina ir pagreitina darbo užmokesčio skaičiavimo procesą, nes automatizuoja skaičiavimus, generuoja darbo užmokesčio žiniaraščius ir tvarko išskaičiuojamus mokesčius. Šis automatizavimas padeda įmonėms sutaupyti daug laiko ir pastangų, todėl žmogiškųjų išteklių ir finansų komandos gali sutelkti dėmesį į strategiškai svarbesnę veiklą. Be to, racionalizuodamos darbo užmokesčio operacijas, įmonės gali sumažinti administracines išlaidas, susijusias su rankiniu darbo užmokesčio tvarkymu. Mokesčių ir darbo teisės aktų laikymasis: Įmonėms labai svarbu laikytis mokesčių ir darbo teisės aktų, kad išvengtų baudų ir teisinių problemų. Programinė įranga Cherry Team seka besikeičiančius mokesčių įstatymus ir darbo reglamentus, užtikrindama tikslius skaičiavimus ir teisinių reikalavimų laikymąsi. Programa gali dirbti su sudėtingais mokesčių scenarijais, pavyzdžiui, keliomis mokesčių grupėmis ir įvairių rūšių atskaitymais, todėl užtikrina atitiktį reikalavimams ir kartu sumažina klaidų riziką. Ataskaitų rengimas ir analizė: Programa Cherry Team siūlo patikimas ataskaitų teikimo ir analizės galimybes, suteikiančias įmonėms vertingų įžvalgų apie darbo užmokesčio duomenis. Ji gali generuoti ataskaitas apie įvairius aspektus, pavyzdžiui, darbo užmokesčio paskirstymą, išskaičiuojamus mokesčius ir darbo sąnaudas. Šios ataskaitos leidžia įmonėms analizuoti darbo užmokesčio tendencijas, nustatyti tobulintinas sritis ir priimti pagrįstus finansinius sprendimus. Pasinaudodamos duomenimis pagrįstomis įžvalgomis, įmonės gali optimizuoti savo darbo užmokesčio strategijas ir veiksmingai kontroliuoti išlaidas. Integracija su kitomis sistemomis: Cherry Team programinė įranga dažnai sklandžiai integruojama su kitomis personalo ir apskaitos sistemomis. Tokia integracija leidžia automatiškai perkelti atitinkamus duomenis, pavyzdžiui, informaciją apie darbuotojus ir finansinius įrašus, todėl nebereikia dubliuoti duomenų. Supaprastintas duomenų srautas tarp sistemų padidina bendrą efektyvumą ir sumažina duomenų klaidų ar neatitikimų riziką. Cherry Team atlyginimų apskaičiavimo programa įmonėms teikia didelę naudą - automatiniai ir tikslūs skaičiavimai, laiko ir sąnaudų taupymas, atitiktis mokesčių ir darbo teisės aktų reikalavimams, ataskaitų teikimo ir analizės galimybės bei integracija su kitomis sistemomis. Naudodamos šią programinę įrangą įmonės gali supaprastinti darbo užmokesčio skaičiavimo procesus, užtikrinti tikslumą ir atitiktį reikalavimams, padidinti darbuotojų pasitenkinimą ir gauti vertingų įžvalgų apie savo finansinius duomenis. Programa Cherry Team pasirodo esanti nepakeičiamas įrankis įmonėms, siekiančioms efektyviai ir veiksmingai valdyti darbo užmokestį. https://cherryteam.lt/lt/
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27.08.24 12:50 James889900

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06.09.24 01:44 Celinagarcia

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23.09.24 18:56 matthewshimself

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02.10.24 22:27 Emily Hunter

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31.10.24 00:13 ytre89

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02.11.24 14:44 diannamendoza732

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12.11.24 00:50 TERESA

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21.11.24 08:02 Emily Hunter

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22.11.24 04:41 [email protected]

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22.11.24 23:43 teresaborja

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24.11.24 02:21 [email protected]

I never could have imagined the nightmare of losing access to my digital wallet. All of my cryptocurrency holdings were abruptly imprisoned, inaccessible, and appeared to be lost forever following a catastrophic hardware breakdown. Years of meticulous investment and careful saving were reduced to nothing more than strings of code that I could no longer control, and I could feel the dread and sorrow that swept through me at that very instant. Thankfully, during my worst moment, I came into (TRUST GEEKS HACK EXPERT), a professional service devoted to recovering lost or inaccessible digital data. With optimism, I went out to their team of skilled technologists, laying bare the full nature of my issue. What followed was a laborious, multi-step process that required an almost surgical level of digital forensics and Bitcoin skill. In order to create a thorough profile of my wallet's contents and activities, the (TRUST GEEKS HACK EXPERT) team first thoroughly examined the transaction history and metadata connected to it. Next, they implemented a series of advanced recovery techniques, using cutting-edge software tools to bypass the access barriers that had left me locked out. The entire process was shrouded in secrecy and discretion, with the (TRUST GEEKS HACK EXPERT) team working tirelessly to protect the confidentiality of my sensitive financial information. After what felt like an eternity of nervous anticipation, the day finally arrived when I received the triumphant notification – my wallet had been successfully restored, and all of my precious digital assets had been returned to my control. The sense of relief was indescribable, as I could finally breathe easy knowing that the fruits of my financial discipline had been safeguarded. While the experience of losing access to my wallet was undoubtedly traumatic, (TRUST GEEKS HACK EXPERT) intervention allowed me to emerge from the ordeal with my cryptocurrency holdings intact, and a renewed appreciation for the importance of proactive digital asset management. You can contact Them through EMAIL: [email protected] - TELEGRAM: TRUSTGEEKSHACKEXPERT
25.11.24 02:19 briankennedy

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26.11.24 21:59 [email protected]

In a world brimming with enticing investment opportunities, it is crucial to tread carefully. The rise of digital currencies has attracted many eager investors, but along with this excitement lurk deceitful characters ready to exploit the unsuspecting. I learned this lesson the hard way, and I want to share my story in the hopes that it can save someone from making the same mistakes I did. It all began innocently enough when I came across an engaging individual on Facebook. Lured in by promises of high returns in the cryptocurrency market, I felt the electric thrill of potential wealth coursing through me. Initial investments returned some profits, and that exhilarating taste of success fueled my ambition. Encouraged by a meager withdrawal, I decided to commit even more funds. This was the moment I let my guard down, blinded by greed. As time went on, the red flags started to multiply. The moment I tried to withdraw my earnings, a cascade of unreasonable fees appeared like a thick mist, obscuring the truth. “Just a little more,” they said, “Just until the next phase.” I watched my hard-earned money slip through my fingers as I scraped together every last cent to pay those relentless fees. My trust had become my downfall. In the end, I lost not just a significant amount of cash, but my peace of mind about $1.1 million vanished into the abyss of false promises and hollow guarantees. But despair birthed hope. After a cascade of letdowns, I enlisted the help of KAY-NINE CYBER SERVICES, a team that specializes in reclaiming lost funds from scams. Amazingly, they worked tirelessly to piece together what had been ripped away, providing me with honest guidance when I felt utterly defeated. Their expertise in navigating the treacherous waters of crypto recovery was a lifeline I desperately needed. To anyone reading this, please let my story serve as a warning. High returns often come wrapped in the guise of deception. Protect your investments, scrutinize every opportunity, and trust your instincts. Remember, the allure of quick riches can lead you straight to heartbreak, but with cautious determination and support, it is possible to begin healing from such devastating loss. Stay informed, stay vigilant, and may you choose your investment paths wisely. Email: kaynine @ cyberservices . com
26.11.24 23:12 rickrobinson8

FAST SOLUTION FOR CYPTOCURRENCY RECOVERY SPARTAN TECH GROUP RETRIEVAL
26.11.24 23:12 rickrobinson8

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00:39 [email protected]

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09:10 Michal Novotny

The biggest issue with cryptocurrency is that it is unregulated, wh ich is why different people can come up with different fake stories all the time, and it is unfortunate that platforms like Facebook and others only care about the money they make from them through ads. I saw an ad on Facebook for Cointiger and fell into the scam, losing over $30,000. I reported it to Facebook, but they did nothing until I discovered deftrecoup . c o m from a crypto community; they retrieved approximately 95% of the total amount I lost.