Machine learning is an application of artificial intelligence that has significantly impacted our daily lives, greatly enhancing voice recognition, traffic prediction, and internet fraud detection. At its inception, computer vision, a machine learning technology, allowed machines to “see” and understand their surroundings like humans. The quality and accuracy of the training data of the computer vision model, which is simply composed of annotations of images, videos, etc., significantly impact its performance. Image annotation is the practice of human-level image classification to describe the desired quality of the data. Depending on the quality of your data, the result is used to develop a model and obtain the degree of accuracy required in computer vision applications. In this article, we will explain image labeling and related concepts.
What is Image Labeling?
Image labeling is a method that consists of identifying and tagging diverse details in images. These tags can then be used to train computer vision models on how to identify a specific object in an image. Data labeling in computer vision is adding tags to raw data, including photos and videos. Each tag represents an associated object class with the data. Supervised machine learning algorithms use labels to recognize a specific object class in unstructured data. It enables these models to provide meaning to the data, which aids in model development.
Tags vary between images, and for example, this method is quite straightforward for pictures that only contain a single object like a face, color, or animal. But for instance, identifying and tagging details in a high-dimension picture taken from a landscape is a whole different process. As the pictures grow larger in detail, it becomes increasingly more difficult to identify and tag those specific details in them.
The more details an image has, the more tags it can offer, and subsequently, it will contain more information. For instance, the picture of a packed street contains many details like people’s faces, different types of cars, shop signs, and many more. So, it can take a while and contain a lot of information on what can be seen in the photo, depending on the features in the image and how thorough the description is.
Types of Computer Vision Image Labeling
One of the fundamental tasks in computer vision algorithms is to label images. Here are a few methods used by computer vision systems to label images. The ultimate objective of machine learning algorithms is automatic detection; however, you need lots of previously labeled data to train a model.
Classification of Images
When images are given as input, image classification algorithms can automatically categorize them into one of several labels (also known as classes). For instance, an algorithm could be able to categorize images of vehicles into labels such as “car”, “train”, or “ship”. The same image may have many labels in various circumstances. As in the previous example, this may happen if a single photograph incorporates many vehicles of different kinds.
It is important to manually evaluate images and mark them with labels utilized by the algorithm to generate a training dataset for image categorization. In the case of generating a dataset for transportation-related photographs, a person would be in charge of looking at each image and assigning the proper label—”car”, “train”, “ship”, etc. As an alternative, artificial intelligence approaches can be used to create such a dataset.
A computer vision algorithm is tasked with distinguishing items in an image from the background or other objects in semantic image segmentation. A pixel map of the picture is often created in order to do this, with each pixel carrying a value of 1 if it is a part of the relevant item or 0 if it is not.
The conventional method for handling many items in the same image is to construct separate pixel objects for each object and concatenate them channel-wise.
It is crucial to manually evaluate images and outline the borders of relevant objects to create a training dataset for a semantic segmentation dataset. This generates a pixel map that humans have validated and may be used to train the model. As an alternative, creating synthetic images with defined object boundaries is feasible to produce pixel maps.
Identifying an item in an image and its placement inside the image frame is the function of an object detection algorithm. Bounding boxes are typically used to define an object’s location. The smallest rectangle that completely encloses an item in a picture is known as a bounding box.
A bounding box technically refers to a set of four coordinates that are associated with a label that identifies the object’s class. Typically, bounding box coordinates and labels are saved in a JSON file using a dictionary format. The dictionary file’s key is the image ID or number.
The image below depicts a scenario with several bounding boxes designating various objects.
The task of locating persons in an image is given to the human pose estimation algorithm. It attempts to recognize several crucial human body parts and uses them to determine the subject’s pose in an image (for instance, standing or lying down).
Images of individuals with manual annotations identifying the important points of bodies seen in the image make up a training dataset for pose estimation. Fundamentally, pose annotations are labels that are matched to coordinates that specify which part of the human body is being referenced (for instance, the right arm). Creating artificial images of individuals using known coordinates for important body parts is also possible.
How to label images for computer vision tasks?
Let’s review a few pointers for appropriately labeling images, knowing we have that in mind. Uncertain about which pictures to label first? Think about the use of active learning in computer vision.
1. Locate every subject of interest in each picture
Computer vision models are developed to discover which pixel patterns relate to an object of interest. Because of this, we must label each time a particular object appears in our photographs if we want to train a model to recognize it. We will be giving our model false negatives if we don’t label the object in some photographs.
For instance, in a dataset of chess pieces, we must identify the appearance of each and every piece on the board; for instance, we would not name just some of the white pawns.
2. Label the Entirety of an Item
Our bounding boxes should encompass an object of interest in its entirety. When an object is only partially labeled, our model becomes confused about what a full object is made up of.
Consider how a bounding box completely encases any chess piece in our dataset.
3. Label Occluded Objects
Occlusion happens whenever an object in a picture is partially hidden from view because something is obscuring it. Even obscured things should be labeled if possible.
Also, instead of drawing a bounding box for just the section of the occluded object that is partially visible, it is generally best practice to identify the occluded object as if it were fully visible.
For instance, a piece frequently blocks another’s view in the chess dataset. Even if the boxes overlap, both items need labels. Contrary to popular belief, boxes can overlap.
4. Establish Tight Bounding Boxes
Tight bounding boxes are essential for assisting our model in learning exactly which pixels make up an object of interest vs. irrelevant areas of an image. Nevertheless, you should never have a tight box that cuts off a section of the object.
5. Make Unique Label Names
It is preferable to go with the route of being more detailed when choosing a label name for a given object. Being more generic is always simpler to achieve than being more particular, which necessitates relabeling.
Consider making a dog detector as an illustration. While every object of interest is a dog, separating Labradors and poodles into their own class could be a good idea. Our labels might be merged to form the word “dog” while first building the model. But we would have had to completely rename our dataset if we had begun with “dog” and then understood the value of having distinct breeds. For instance, our chess dataset has a white and black pawn. Combining these to be pawns or even all classes to be pieces is always possible.
6. Maintain Clear Labeling Instructions:
We will inevitably need to expand our dataset; this is a crucial component of model improvement. We make the most of our labeling time using active learning strategies. In order to develop and maintain high-quality datasets, it is crucial for both our present selves and our coworkers to have clear, reproducible, and shareable labeling instructions.
Several of the strategies we’ve covered here should be used, such as labeling every object, making labels tight, and labeling everything. Always err on the side of greater specificity rather than less.
7. Use These Labeling Tools:
How should we label our data, given that we are aware of useful labeling techniques? While labeling ourselves, we can add and update labels using labeling tools.
Dataset Analysis and Audit
Visually review distributions of label counts per category to identify underrepresented classes needing oversampling. Check for patterns indicative of bias like consistent backgrounds disproportionately skewing the data. Conduct periodic spot checks on subsets to confirm consistent quality over time, retraining annotators as needed. Well-managed datasets should evolve organically as new cases expand coverage.
The Importance of Image Labeling
As a direct result of the increased deployment of artificial intelligence (AI) technologies, interest in image labeling is rising. Applications for computer vision may be found in many different fields, including the development of autonomous cars, product quality control during production, and investigation of suspicious activities in video surveillance.
Data scientists must first train a model to detect images and objects to create an AI computer vision system. Using cameras, a computer vision can “see”, but without training and the proper models, it cannot comprehend what it sees and takes proper action. A labeled image training dataset enables a deep-learning computer vision algorithm to identify images. The authentic inputs that the algorithm is likely to encounter are represented by relevant images or videos that data scientists gather. Then, data labelers examine these pictures and give them precise labels.
Methods of Image Labeling:
Manual Image labeling
This is a common method for Image labeling and manual annotation. This is the process of manually defining labels for the entire image or drawing regions within an image and adding text descriptions for each region. Image annotation specifies a standard that a computer vision algorithm is trying to learn from, that is, any type of error in labeling is acceptable by the algorithm and reduces its accuracy. This means that accurate Image labeling is an important task in neural network training.
Manual annotation is usually aided by tools that allow operators to cycle through large numbers of images, draw logic on an image and assign labels, and store this data in a standard format that can be used for data training.
Manual annotation comes with several challenges:
- If there are many annotators, the labels can be inconsistent, and to solve this problem, the images must be labeled multiple times by majority vote.
- Image labeling manually is time consuming. Annotators must be carefully trained and this process requires many iterations. This can delay the time to market for computer vision projects.
- Manual Image labeling is expensive and difficult to scale for large datasets.
Semi-automated Image labeling
Manual image annotation is very time-consuming and for some computer vision algorithms, it is difficult for humans to achieve it. For example, some algorithms need pixel maps that show the exact boundaries of several objects in one image.
Automatic annotation tools can assist manual annotation by attempting to detect object boundaries in an image and provide a starting point for the annotator. Automatic annotation algorithms are not exact but can save time for human fluctuation margins by providing at least a partial map of the objects in the image.
Synthetic Image labeling
Synthetic image labeling is an accurate and cost-effective technique that can replace manual annotation. This technique involves the automatic generation of images similar to the real data, according to the parameters determined by the operator. Combining the above approaches allows utilizing automation while reserving human judgment for challenging cases For example, it is possible to create a Synthetic database of real objects or human faces that look like real objects but are not identical. The main advantage of synthetic images is that the labels are already known, for example, the operator automatically generates images containing tables or chairs, in this case, the image generation algorithm can automatically define the bounding boxes of tables and chairs. Provide the chair in each image.
Common methods for producing Synthetic images are as follows:
Variational Autoencoders: These are algorithms that start from existing data, create a new data distribution and return it to the original space using the encoder-decoder method.
Generative Adversarial Networks: These are models that pit two neural networks against each other. One neural network tries to create fake images while the other one tries to distinguish between real and fake images. Over time, the system can produce near-realistic images that are hard to distinguish from reality.
Neural Radiation Fields: This model takes a set of images that describe a 3D scene and automatically provides new and additional views of the same scene. It works by calculating a 5D ray function to generate each voxel of the target image.
Defining Classification Categories
The first step of how to label images for image classification is
determining the appropriate class labels and hierarchy for the problem. Highly
granular individual species or product labels provide more precision but
require larger datasets and may be unnecessary. Broader groupings like tree
genus or vehicle type are often sufficient. Seek the optimal granularity
balancing accuracy needs and annotation complexity.
Handling edge cases between overlapping classes can be challenging.
Provide clear guidance like only labeling front-facing cats, not side profiles.
Ambiguity is reduced by picking representative archetype images as examples
during instruction. Think critically about the core categories that matter for
the use case.
annotation instructions enable consistent labeling. Document detailed
requirements on bounding box placement, such as tightly cropped around objects
versus enclosing full extent. Set rules regarding occlusion, cropping, poses,
and unusual examples. Mandate minimum box sizes to avoid unhelpful tiny
segments. Capture rare but important oddities through mandated outlier sampling
of under-represented items. Re-annotate samples periodically to confirm
instructions are being followed uniformly. Well-defined guidelines establish
reliable training data.
Annotation Interface Design
An intuitive annotation interface optimizes the workflow. Keyboard
shortcuts placing commonly used functions front and center like creating boxes
or switching classes avoids mousing. Embedded validation checks flag errors
immediately, such as boxes too small or missing required views. Dedicated tools
like polyline tracing enable efficient segmentation. Displaying annotation
history provides context. The UI hugely impacts annotator productivity and
Managing Ambiguity and Errors
Ambiguity is inevitable for some percentage of images. Employ
consensus strategies where two annotators provide labels and adjudicate
differences. Another approach is certainty scoring to capture annotator
confidence. Qualification testing during onboarding identifies poor performers.
Inter-rater reliability metrics quantify labeling consistency. Analyze these
metrics to improve guidelines and training. Some degree of error is unavoidable
but minimizing it improves model robustness.
Label Image for Object Detection Tools
Specialized label image for object detection aim to streamline image annotation:
- Cloud-based platforms like Labelbox provide intuitive browser-based interfaces, collaboration, and automation to label image datasets at scale. Many integrate directly with popular machine learning frameworks.
- OpenCV, VOTT, and other open source tools offer annotation plug-ins usable locally or in custom workflows. Integrations with modeling pipelines facilitate rapid training iterations.
- Segmentation-specific tools facilitate detailed polygon outlines for pixel-level category masks beyond bounding boxes. Interfaces are optimized for efficient object tracing.
- Automated recommendation systems suggest potential boxes to labelers using simple detection models for faster human verification. Additional logic improves annotation consensus between labelers.
- Programmatic APIs enable annotation automation and integration natively into data pipelines. Some companies offer manual labeling via APIs too.
Different Types of Image Labeling
Annotation and tagging are the two main types of image labeling. The act of categorizing complete images with particular concepts is referred to as tagging. Images of various animals, for instance, are labeled based on the animal that is depicted. Then, using this data collection, you may train machines to spot these creatures in other images. Additionally, image tagging makes it simpler to navigate via your image library. The images are shown as results when the tagged terms are used as keywords in an internal search. Additionally, web crawlers utilize this data while indexing a website, which may improve your SEO ranking.
Image annotation, on the contrary, labels specific sections of an image. This indicates that only one label has been given to a portion of the image rather than the whole thing. This may manifest in a variety of ways, including the following:
One of the most common types of image labeling is via bounding boxes. In images of traffic, rectangular boxes are put around certain objects, such as vehicles, bicycles, and people. As a result, the AI can detect these patterns in various settings and learn to apply this information to new images.
Polygons are used to annotate certain objects inside an image, just like bounding boxes are. The lines are drawn closer to the object’s boundaries and can be used to label items that do not fit within rectangular boxes, while polygons allow for more precision.
Semantic segmentation enables annotators – labelers – to categorize each image pixel in accordance with a predefined set of tags. For instance, the pixels in an aerial shot of a neighborhood that represent streets, homes, cars, or gardens can be noted. This gives you incredibly detailed labels, which are crucial when an image’s surrounding environment is important.
Annotating key points can be utilized to find tiny objects and shapes. For this purpose, dots are positioned on particular areas of the image, such as a person’s lips, brows, or eyes. Machines may learn to distinguish between various moods and detect facial characteristics from various angles using this information.
Image Labeling Techniques
Here are some of the most important techniques that can be used in image labeling.
1. Automated Image Labeling Techniques:
Automated methods, including pre-trained models, assist in image labeling tasks. Explore the advantages and limitations of automation in image labeling.
2. The Role of Human-in-the-Loop Labeling:
Human-in-the-loop labeling involves human annotators working with AI algorithms to improve labeling accuracy. This approach addresses complex image annotations and ensures high-quality datasets.
3. Challenges in Multi-Class Image Labeling:
Multi-class image labeling poses difficulties in labeling images with multiple objects or diverse classes. Strategies to handle class imbalance and ensure accurate labeling are essential.
4. Crowdsourced Image Labeling:
Crowdsourcing is a cost-effective and scalable approach to image labeling. Popular crowdsourcing platforms facilitate image labeling tasks, and quality control is crucial to maintain accuracy.
5. Transfer Learning for Image Labeling:
Transfer learning techniques accelerate image labeling tasks by fine-tuning pre-trained models. Successful transfer learning applications in various industries are explored.
Image labeling applications
It is now feasible to train machines to perceive and comprehend the visual environment like humans through digital photos, videos, and deep learning as a service model. Image labeling can help with this. The quality of these labels, whose use goes beyond the categorization of various objects in an image and the classification of distinct classes, determines the accuracy of the computer vision (CV) model.
Let’s look at some applications of this technique to understand better how human-powered AI and ML automation enhances real-life processes:
Satellites, drones, and remote sensors produce ecological imagery datasets to monitor changes over time. Labeling allows for identifying features like land usage, vegetation types, and the presence of disaster damage. Image labeling supports sustainability initiatives.
Security and surveillance
Surveillance cameras generate torrents of video data. Image labeling helps train AI to analyze this content by annotating objects, actions, facial attributes, and spatial relationships. This enables intelligent monitoring to automate threat detection and investigations. Privacy protections during labeling are critical.
Self-driving cars use cameras, lidar and other sensors to interpret driving environments. Image labels help identify objects like pedestrians, traffic signs, and lane markings that are critical to navigating safely. This powers self-driving algorithms to understand complex roadways. Startups like Scale AI provide annotators and tooling to label massive autonomous driving datasets.
The insurance industry handles vast amounts of visual data from documents to damage assessments, which presents an opportunity to leverage computer vision and AI to optimize processes. However, training computer vision models requires labeled image datasets in order to accurately interpret content. Image labeling techniques that classify document types, extract handwritten text, identify damage areas, detect fraud patterns, and recognize personal ID information enables insurers to implement AI-powered automation for faster claims and underwriting, improved customer experiences, and reduced fraud. Insurance companies that invest in scaling image labeling capabilities position themselves to lead the industry into an era of optimized operations and reductions in expenses powered by computer vision.
In factories and warehouses, computer vision guides robots to grasp objects, detect bin-picking points, and inspect product quality via image labels. Training data consists of images manually annotated with object bounding boxes, edges, surface defects, and more. As robotics proliferate across settings like retail and logistics, labeled image data will fuel advances.
Consistent, high-quality image labels are imperative:
- Inter-rater consensus metrics like overlap, agreement percentage, Cohen’s kappa, quantify consistency between different annotators.
- Statistical sampling and auditing methods selectively check subset validity.
- Automated validation uses basic detection models to flag potential errors through outlier detection.
- Progressive sampling focuses annotator time on uncertain and difficult cases.
- Continuous monitoring through periodic statistical checks and spot validation prevents label drift over time.
Careful quality control builds confidence in annotated dataset integrity to train robust models.
Identifying the main challenges associated with Image Labelling Services
To say that image labelling service is easy is not true; Image labelers have to overcome a number of difficulties throughout the labelling cycle, with a constant focus on ensuring 100% labelling accuracy. These 5 most important issues in image annotation are discussed below.
Data relevance and quality assurance
Image labelers are concerned about the quality of the image data, especially as the project matures. As a result, extracting pertinent insights or targeting sections of photos can prove costly. Data quality management requires the consideration of several factors and the use of a variety of standardization techniques.
Human labelers find it difficult to reliably capture image attributes, as they risk missing key image objects when processing hundreds of thousands of photos. As a result, maintaining accuracy over time can be a challenge and a potential source of inaccuracies.
Cost management and time optimization
Even if you are confident in your ability to invest in and develop your own in-house staff, calculate the time required to create the appropriate in-house setup. In addition, you will need to identify and select the best data annotation tools, create robust human-in-the-loop procedures, and be prepared to modify your infrastructure to make the whole framework adaptable and scalable.
Ultimately, it all comes down to your estimation skills, forcing you to produce estimates that don’t waste your resource budget. Your other critical business activities will suffer from overestimation, while labelling will suffer from underestimation.
Maintaining data confidentiality
AI stakeholders are always concerned about the security of images, because they don’t want the private information they contain to be misused. As a result, they are now responsible for having security systems that handle and store images securely. In order for such a security framework to function as a licensed framework for image labelling service, it must comply with security requirements such as GDPR, CCPA and other compliance requirements.
Consequently, a secure data storage system is necessary to ensure data confidentiality. However, the issue of security still arises even if you do not decide to perform image annotation yourself. The simple explanation is that you are giving an external party access to all your data and you are checking the annotator’s security system.
Keep the process moving
To achieve 100% accuracy, the image labelling service requires participants to adhere to a feedback loop. While it would be great to have correctly labelled photos all at once, this is never realistic when thousands of images need to be labelled in a given amount of time.
Computer vision specialists work with image annotators to correct poorly labelled, incorrectly labelled or incompletely labelled images. Since poorly labelled datasets are incompatible with computer vision algorithms, AI experts have to explain every little element of the modelling process to the labelers.
As the process progresses, fewer images are annotated than intended or projected. Ultimately, the whole AI modelling process suffers, which affects the speed of training.
Developing a scalable support system
Consider a scenario where you have been annotating 5,000 photos per day for more than six months. You are confident that the number of deliveries will only vary by a few hundred at most. Suddenly, however, new members of your AI team have suggested a complete overhaul of the current models. This also affects your image tagging practices. Your AI team is now capable of creating the proposed AI model, but they ask you to help them by providing them with 20,000 photos. Don’t you already feel constrained by the current situation? Scalable image tagging is therefore a big risk.
Difference between image Annotation and image Labeling
Image labeling and annotation are terms frequently used interchangeably to describe the act of tagging or labeling an image available in various forms. Image annotation is the process of annotating an image so that machine learning as a service algorithm can interpret and memorize the input image. it, also known as image tagging, is the process of assigning meaning to various forms of data to train a machine-learning model.
Labeling is a fundamental component of supervised machine learning, and many sectors still rely significantly on manually annotating and labeling images. Labels are used to identify dataset properties for NLP algorithms, while image annotation is useful for visual perception models.
Labeling is more difficult to understand than annotation. Annotation assists computer vision in recognizing relevant images, whereas labeling is used to teach sophisticated algorithms to discover patterns in the future. Both stages must be carried out precisely to ensure that something useful is extracted from the image to construct an NLP-based AI model.
Image annotation is a critical component in creating training data for computer vision. Annotated images are needed to teach machine learning algorithms to view the world as we do. The goal is to create intelligent machines to learn, act, and respond like people. Annotation is a technique used in supervised machine learning to help machines comprehend and recognize input images and proceed accordingly. Labeling is a method for identifying essential characteristics in an image while minimizing human intervention.
Read more: What is Image Annotation?
Annotation requirements differ across domains. Medical imaging needs precise delineation of lesions and anatomical structures. Retail entails fine-grained differentiation of products by type, brand, and attributes. Autonomous vehicle systems demand nuanced road object categories and conditions indicating drivability. Domain experts should inform guidelines tailored to the specific classes, degrees of precision, and attributes necessary for the application. This boosts model utility.
Active learning maximizes human insight by selectively identifying useful data for annotation:
- Uncertainty sampling focuses labeling on ambiguous samples where the model lacks confidence.
- Representativeness ranking annotates items covering the data distribution.
- Query-by-committee identifies areas of disagreement between models.
- An initial unlabeled dataset pool can be preprocessed by a simple trained model to guide annotation.
- Human-in-the-loop systems dynamically integrate new labels into models using continuous training to guide subsequent annotation.
Strategic iterative annotation powered by active learning minimizes wasted labeling effort while accelerating model improvement.
Legal and Privacy Considerations in Image Labeling
There are some legal and privacy consideration in image labeling that need to be mentioned.
Data Privacy and Consent:
Ensuring data privacy and obtaining consent from individuals whose images are used in labeling is critical to comply with data protection regulations.
Intellectual Property Rights:
Addressing ownership and licensing of labeled data to prevent copyright infringements and disputes.
Ethical Use of Labeled Data:
Ensuring the ethical use of labeled data, particularly when sensitive or personal information is involved.
The Image Labeling Service at Saiwa
In machine vision applications, three types of labeling are commonly used: Classification is the first step, followed by object detection and image segmentation. Saiwa supports these three labeling types with three annotation services: classification annotation, bounding box annotation, and boundary annotation.
Classification is a term used to describe whole-image labeling that simply identifies features in the input images. Each image has a single label. This form of labeling is utilized whenever a machine learning model is programmed to detect similarities between unlabeled images and known labeled images.
This type of labeling is concerned with identifying and detecting things of interest in an image. In this case, the labeling software should include tools for drawing bounding boxes around the instances of objects in addition to labels.
when items are detected at the pixel level. In this step, the borders of objects inside an image are defined and afterward discovered for unlabeled images. The margins of the items may be uneven.
The process of image labeling at Saiwa
Image labeling in Saiwa is a simple technique that can be completed in several phases.
Here are some phases to manually label an image:
- Select the image dataset
- Establish the label classes.
- Use labeling software to label the images.
- Save the labeling data in a training format (JSON, YOLO, etc.).
Read more: online image labeling
The features of the Saiwa image labeling service
- Promote the use of the three most common types of labeling.
- For complicated situations, an interactive interface with a few clicks is required.
- Save to commonly used labeling formats
- Labels with various overlapping and advanced
- The results can be exported and archived locally or in the individual’s cloud.
- The Saiwa team can customize services through the “Request for Customization” option.
- View and save the labeled images.
Most frequent questions and answers
Image labeling helps to improve the accuracy and efficiency of image-based applications by providing a standardized and consistent way to identify and classify images. It also helps reduce the time and effort required to search through large sets of images manually.
Object detection is a type of image labeling that involves identifying and localizing objects within an image. It is used in applications such as autonomous driving and surveillance systems.
Semantic segmentation is a type of image labeling that involves labeling each pixel of an image with a corresponding class label. It is used in applications such as medical imaging and satellite imagery analysis.
Image classification is a type of image labeling that involves assigning a single class label to an entire image. It is used in applications like image search engines and content-based image retrieval.
Instance segmentation is a type of image labeling that involves identifying and localizing each individual instance of an object within an image. It is used in applications such as robotics and industrial automation.
Image labeling can be done manually by humans or automatically by computer algorithms. Manual image labeling involves annotating images with descriptive text or tags, while automatic image labeling involves using machine learning algorithms to classify and label images.
Some challenges of image labeling include variability in image quality and content, ambiguity in labeling criteria, and the cost and time required to label large sets of images. However, these challenges can be overcome using advanced image labeling tools and techniques.