Data Annotation by EPIC Translations: Image Annotation Data for AI & ML

EPIC Translations provides image annotation services for deep learning and computer vision. All in one place! Our skillful team of image annotators makes sure your machine learning projects run quickly and efficiently following all your annotation requirements. Image annotation is a key technique used to train Machine Learning algorithms to learn to see the world as we do and this is heavily reliant on the accuracy of its training data. Invest in talented and experienced annotators to get precise and cost-efficiency annotations. We support a wide variety of image annotation services that will match your project’s needs, including 2D bounding boxes, 3D cuboids, lines and splines, polygons, semantic segmentation, image classification, pixel-precise/pixel-wise segmentation, and more. Image Annotation: Annotation with a bounding box is the most commonly used and simplest type of image annotation. It refers to a rectangle that has to be drawn around the edges of each object in an image. The goal is to help us detect and recognize the different classes of objects, it’s found in object classification, localization and detection. 3D bounding boxes show approximate depth of the target objects being annotated making sure to place anchor points at the object’s edges. Our annotators are ready to precisely draw a bounding box 2D or 3D around the object you want to annotate within your images. Polygons: Polygon annotation is important because not every object may fit precisely in a bounding box. They are a much more precise way to annotate objects by only including pixels that belong to them. Polygon annotation provides the flexibility to plot points on each vertex of the target object. This annotation method allows all of the object’s exact edges to be annotated, regardless of its shape. Perfect for objects like fruits, trees, landmarks, houses and much more. Our annotators have the high level of precision required for this kind of project. Image segmentation: Image segmentation takes image annotation to a new level by finding out accurately the exact boundary of the objects in the image. In image segmentation, each pixel is classified into a certain class. There are two types of segmentation techniques. Semantic segmentation: It assigns a general label to all instances of an object. For example, if the color blue is assigned to cars on an image, all cars will be blue within the image. Instance segmentation: It gives a unique label to every instance of a particular object in the image. For example, every car on an image will have different colors. Our annotators will meticulously annotate the specified objects in your images with pixel-wise accuracy. Lines and splines: Lines and splines annotation is the labeling of straight or curved lines on images. They are mainly used for lane and boundary recognition. As well, they are also often used for trajectory planning. From autonomous vehicles and drones to robotics in warehouses and more, lines and splines annotations are useful in a wide variety of use cases. WHAT DOES EPIC TRANSLATIONS BRING TO THE TABLE? Machine Learning Pipeline – That is, from Data Collection, Data Preprocessing, selection of algorithm that is well suited to deliver the best results based on the challenge, tuning and tweaking of hyperparameters, creation of a multialgorithm pipeline for comparison of different algorithms and how they perform, and finally the delivery of the algorithm to a real-world scenario by creating APIs that can manipulate and translate data to a given output. The creation of machine learning packed Microservices that can be used in real-world deployment of the already trained algorithms. Machine Learning Consultation- Based on our knowledge of the machine learning infrastructure we can offer a great deal of expertise on the subject matter. Sentiment Analysis – Natural Language Processing (NLP) being one of the disciplines of machine learning gives us a great opportunity to apply sentiment analysis especially in the understanding of text and its context. WHAT TYPES OF PROJECTS HAVE WE WORKED ON? Using Sentiment Analysis in the categorization of twitter data that we collected from tweepy (Twitter API) into either hate speech that could lead to political instability in a developing country or a neutral sentiment. The collection of data, labelling of data, development of machine learning algorithm that was used as a recommender system to advise potential tenants on places that they could rent out depending on a few preferences that they gave and its deployment using an API. The development of Reinforcement Learning algorithm that could predict the oil pressure rates based on various factors for use in oil production purposes. The collection of audio data, its synthesis, training and deployment into a personal “SIRI” or “CORTANA”. A machine learning project to demonstrate and predict the COVID-19 outbreak. A machine Learning project based on various crop diseases that was used to categorize crop diseases based on images and their cross relationship with the training data. WHAT TYPES OF SOFTWARE AND TOOLS DO WE USE FOR IMAGE ANNOTATION? Some of the software and tools we use are: 1. LabelImg 2. Labelme 3. Labelbox 4. CVAT 5. Dataloop 6. VGG 7. And more…

EPIC Translations 为深度学习（Deep Learning）与计算机视觉（Computer Vision）提供一站式图像标注服务。

我们经验丰富的图像标注团队能够严格遵循您的所有标注要求，确保您的机器学习（Machine Learning）项目高效推进。图像标注是训练机器学习算法以人类视角感知世界的核心技术，其效果高度依赖训练数据的准确性。投入于才华横溢且经验丰富的标注人员，可获得精准且具成本效益的标注结果。

我们提供多元化的图像标注服务，可满足您项目的各类需求，包括二维边界框、三维立方体、线条与样条曲线、多边形、语义分割（Semantic Segmentation）、图像分类（Image Classification）、像素级分割等。

### 图像标注
边界框标注是最常用且最简单的图像标注类型，即围绕图像中每个对象的边缘绘制矩形框。其目标在于辅助检测与识别不同类别的对象，广泛应用于对象分类、定位与检测任务中。

三维边界框可呈现被标注目标对象的大致深度，并确保在对象边缘放置锚点。我们的标注人员可精准绘制图像中目标对象的二维或三维边界框。

### 多边形标注
多边形标注至关重要，因为并非所有对象都能精准适配边界框。该方法仅包含对象所属像素，是一种更为精准的标注方式。多边形标注支持在目标对象的每个顶点灵活布点，无论对象形状如何，均可实现其精确边缘的完整标注，适用于水果、树木、地标、房屋等各类对象。我们的标注人员具备此类项目所需的高精度标注能力。

### 图像分割
图像分割（Image Segmentation）通过精准识别图像中对象的精确边界，将图像标注提升至新高度。在图像分割中，每个像素均被归类至特定类别。分割技术主要分为两种：
- 语义分割：为同一类对象的所有实例分配通用标签（例如，若将蓝色分配给图像中的汽车，则所有汽车均会显示为蓝色）；
- 实例分割：为图像中特定对象的每个实例分配唯一标签（例如，图像中的每辆汽车均会显示为不同颜色）。
我们的标注人员将以像素级精度细致标注您图像中的指定对象。

### 线条与样条曲线标注
线条与样条曲线标注是对图像中的直线或曲线进行标记的技术，主要应用于车道与边界识别，也常用于轨迹规划。从自动驾驶车辆、无人机到仓库机器人等领域，线条与样条曲线标注具有广泛的应用场景。

### EPIC Translations 的核心优势
- **机器学习 Pipeline**：涵盖数据采集、数据预处理（Data Preprocessing）、基于任务挑战选择最优算法、超参数（Hyperparameter）调优、构建多算法 pipeline 以对比不同算法性能，最终通过创建可处理并转换数据至指定输出的 API（Application Programming Interface），实现算法向真实场景的落地。此外，我们还构建了封装机器学习功能的微服务（Microservices），可用于已训练算法的实际部署。
- **机器学习咨询**：基于我们对机器学习基础设施的深入理解，我们可提供该领域的专业咨询服务。
- **情感分析（Sentiment Analysis）**：自然语言处理（Natural Language Processing, NLP）作为机器学习的分支之一，为我们应用情感分析提供了契机，尤其适用于文本及其语境的理解。

### 我们参与过哪些类型的项目？
1. 利用情感分析对通过 Tweepy（Twitter API）采集的推特数据进行分类，识别可能引发发展中国家政治不稳定的仇恨言论或中性言论；
2. 数据采集、标注及机器学习算法开发，构建推荐系统以根据潜在租户的偏好为其提供租房建议，并通过 API 实现部署；
3. 开发强化学习（Reinforcement Learning）算法，基于多种因素预测油压速率，应用于石油生产领域；
4. 音频数据采集、合成、训练及部署，打造个人版“SIRI”或“CORTANA”；
5. 机器学习项目，用于展示并预测 COVID-19 疫情的爆发；
6. 基于多种作物病害的机器学习项目，通过图像及其与训练数据的交叉关联对作物病害进行分类。

### 我们使用哪些图像标注软件与工具？
我们使用的部分软件与工具包括：
1. LabelImg
2. Labelme
3. Labelbox
4. CVAT
5. Dataloop
6. VGG
7. 以及更多……