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Automatic assignment of relevant tags and keywords to the input image
Harnessing the power of visual assets is crucial for businesses across various industries. However, as digital libraries grow exponentially, manually assigning tags and keywords to images becomes a daunting and time-consuming task. That's where automatic tagging can help.
The Image tagging ML model is a powerful machine learning algorithm integrated into our service. It automatically analyzes the pixel content of images, extracts their features, and assigns relevant tags or keywords to facilitate improved organization and utilization of visual assets. With this model, businesses can unlock a new level of efficiency and productivity.
The process of AI tagging involves object detection techniques that detect objects, scenes, and concepts within the images. Based on this analysis, appropriate tags are assigned to each image, enabling proper asset management and content discovery.
The AI tagging model can provide support for a wide range of typical use cases, including:
Efficient organization - Automatically assigned tags enable efficient organization of visual assets by categorizing them based on specific objects, scenes or concepts. Users can easily navigate through their image library and quickly locate the desired assets.
Accelerated search - Users can retrieve images based on generated tags or keywords. This accelerates the search process, making it effortless to find the required images in large collections.
Content Recommendation - Visually similar assets can be identified based on shared tags. Users can discover related content and leverage the full potential of their visual assets.
Visual content analysis - The model can aid in understanding visual trends, analyzing customer preferences and performing visual data analytics.
An up-to-date reference with all API endpoints is available here:
An ML model that extracts the most prominent colors in an image
The model analyzes the colors present in the input image and determines if they fall into several predefined ranges. It then compares the number of pixels in those ranges and outputs color names that were detected as visually dominant.
The pixels of the image are examined to identify the most prominent colors present. Multiple factors such as the distribution and frequency of different color values are taken into account to determine the dominant colors.
To find the color of a pixel, all values in the image are converted to an alternative color representation that is designed to be more closely aligned with the way human vision perceives colors. This enures high-quality results.
Some typical use cases for this model include:
Visual search and filtering - Users can search for images based on specific dominant color criteria. For example, they can search for images dominated by a particular color to find assets that align with a specific theme or aesthetic.
Color-based categorization - The feature facilitates the automatic categorization of images based on their dominant colors. This allows for easier location and grouping of images with similar color schemes.
Branding Enhancement - Teams can utilize the dominant color information to ensure consistent branding and visual coherence. They can search for images that match their brand identity and integrate them into their campaigns.
An up-to-date reference with all API endpoints is available here:
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An ML model that detects logos of popular brands, visible in the input image
In today's digital landscape, brands are a vital aspect of any business. A brand logo serves as a symbol of identity, recognition, and trust. Managing and protecting brand assets is essential for maintaining brand consistency, ensuring compliance, and maximizing brand visibility. Our model is designed to streamline this process by automating the detection and categorization of brand logos within your digital assets, facilitating effective brand asset management.
The Brand recognizer is powered by state-of-the-art machine learning algorithms, and trained on vast datasets containing diverse brands from a wide selection of industries. This enables the model to accurately recognize and detect brand logos with varying sizes, orientations, and backgrounds within the provided images.
The model supports a vast number of popular brands and enables efficient brand analytics and monitoring, providing valuable insights and efficient brand management within our service.
The following are some use cases, suitable for our Brand recognition model:
Brand monitoring and compliance - By identifying brand logos in user-generated content, the model assists in brand monitoring, ensuring compliance with brand guidelines and protecting brand integrity.
Brand analytics - The model provides insights into brand visibility and presence by analyzing the frequency and distribution of detected brand logos across digital assets. This information can be used for brand performance evaluation and marketing strategies.
Competition Analysis - By recognizing competitor brand logos within the digital assets, the model aids in conducting competitive analysis, identifying market trends, and evaluating brand positioning.
Image Retrieval - The Brand Recognizer enables efficient searching and retrieval of digital assets by logo-based queries. Users can locate specific assets based on recognized brands.
Digital rights management - Copyright and licensing agreements can be enforced with automatic logo detection and tracking. This ensures compliance with intellectual property regulations.
An up-to-date reference with all API endpoints is available here:
A ML model that evaluates the technical quality of images by analyzing their visual attributes
The quality of digital content is a critical factor in delivering satisfactory user experience and achieving branding goals. Our Image quality assessment (IQA) model is a machine learning solution designed to evaluate the visual quality of images.
When an image is fed into the system, the model processes it to assess various quality attributes and generates a quality score that reflects a weighted combination of those attributes, providing a precise assessment of the image's technical quality.
The attributes include multiple factors such as sharpness, blurriness, noise, dynamic range, contrast, distortion, etc. By providing a thorough image quality assessment, the model greatly enhances the management of digital assets and offers indispensable aid in content selection and optimization.
Image quality assessment finds application in various domains and industries where image quality is a top priority:
Stock photography - Stock image providers can use the model to rate and categorize the images in their libraries, thus helping customers find ones with superior quality for their projects.
E-commerce - E-commerce platforms can ensure product images meet quality standards, improving the visual appeal of product listings and driving sales.
Publishing - Media outlets and publishing companies can automate the assessment of images for articles so their publications contain high-quality assets.
Advertising - Advertising agencies can use more easily evaluate the technical quality of campaign visuals, helping maintain brand consistency and professionalism.
Digital marketing - Marketers can optimize their social media and online content for greater impact and engagement.
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
A set of ML models that accurately detect human faces and predict crucial characteristics like facial landmarks, expression, ethnicity, age, and gender
The Face analyzer consists of several cutting-edge ML models. Its primary function is to detect visible human faces in images and predict some facial characteristics that are deemed important.
Leveraging state-of-the-art deep learning algorithms and neural networks, the Analyzer accurately identifies and analyzes faces, extracting the following information for each face:
position in the image (bounding box);
facial landmarks (coordinates of points that map to specific facial structures on the face);
expression classification (happy, angry, sad, etc.);
ethnicity classification;
age estimation;
gender classification.
Use cases for automatic facial analysis include:
Image tagging and organization - Automatic facial analysis enables users to easily categorize and index images based on expressions, ethnicities, age groups and genders. This streamlines content management, making it easier to locate specific images for various purposes.
Inclusive representation - The Face Analyzer can facilitate inclusive representation in media content. By analyzing facial ethnicities and genders, content creators can ensure diversity and cultural representation in their visual assets, thus promoting inclusivity.
Search Optimization - Automatic tagging and categorization of images based on facial characteristics allow users to find specific faces, expressions, or ethnicities with ease.
An up-to-date reference with all API endpoints is available here:
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Unleashing the power of AI to transform your digital assets
A derivative asset refers to a modified or transformed version of the original digital asset. These derivatives are created to meet specific needs, such as different file formats, resolutions or variations in content. The goal is to make the asset more suitable for various platforms or use cases, without altering the integrity of the original.
For instance, a high-resolution image can have derivatives in different resolutions for web, print or mobile applications. Those assets play a crucial role as they enable efficient distribution and utilization across diverse channels and media.
Derivative generation involves the automated or manual process of creating these derivative assets from the original master asset. They can usually be generated on-the-fly or through predefined workflows. Automation reduces the manual effort required to produce derivatives and ensures consistent output quality.
Additionally, derivative generation allows users to access and use the right version of an asset that suits their specific requirements. This functionality streamlines asset management, reduces duplication, and ensures that the right content is efficiently delivered to the right audience.
Derivative generation can be enhanced and accelerated using different machine learning models. ML models, particularly those in the field of computer vision and image processing, can play a significant role in automating the creation of derivative assets.
A Generative AI model is a type of artificial intelligence algorithm designed to generate new content that resembles or is similar to the data it was trained on. Unlike traditional AI models that are used for classification or prediction tasks, generative models focus on creating new data rather than making decisions based on existing data.
They can be trained to recognize and understand different elements of an image, such as objects, faces, backgrounds, and other features. This understanding allows for intelligent resizing, cropping, background removal and other manipulation that produce derivatives without compromising the overall quality and aesthetics. By incorporating such capabilities, the process of derivative generation becomes more accurate and scalable.
Generative AI models have a wide range of applications, including image synthesis, text generation, music composition, video generation, and more. They are particularly valuable in creative fields and content generation tasks where producing new and original content is essential. However, they can also be used in other domains, such as data augmentation for training other machine learning models or in data generation for simulations and testing.
The following sub pages describe some of the derivative generation models offered by ASK Filerobot.
A system that detects human faces, extracts feature vectors and clusters similar faces to efficiently group images based on the individuals present in them
The Face Clustering ML model is an advanced component offered as a part of our service. With its powerful facial detection and feature extraction capabilities, this model fundamentally changes the way images are organized and grouped based on the individuals present in them.
The model accurately detects visible human faces within images, regardless of their positions or orientations. This crucial first step ensures that all relevant faces are identified for subsequent clustering.
For each detected face, the model extracts a high-dimensional feature vector, representing the unique facial characteristics and attributes of that individual. These feature vectors capture critical facial traits while discarding irrelevant information, making them ideal for face-based comparisons.
Leveraging advanced machine learning algorithms, the model clusters all faces (and the respective images they are part of) based on the similarity of their feature vectors. Faces with similar features are grouped, enabling the automatic identification of distinct individuals in image collections.
The face clustering model can categorize faces without the need for explicit training data on individual identities. This flexibility allows the model to adapt to various datasets and expand its clustering capabilities.
Automatic face clustering can have a significant impact on the following use cases:
Event photography management - Event photographs often contain multiple different individuals. The model simplifies the organization of these images by automatically grouping them based on the distinct people present. This streamlines the curation and delivery of photo collections.
Content organization - Automatic face clustering can make it easier for users to navigate and discover images related to specific individuals.
Photo collections - In personal photo libraries, the face clustering model assists in creating dedicated albums or collections for family members and friends, thus saving users valuable time by automatically organizing photos by individual identities.
An up-to-date reference with all API endpoints is available here:
An ML model classifying an image as pure product image or application image, showing the product in use
The Product type classifier is a machine learning model, specifically designed to classify images into two categories:
Pure product images typically show the product by itself in a clear and uncluttered way. The goal in this case is to focus on and highlight the product features and design.
Product application Images that show the product in use or context, often in a real-world setting. These images are designed to illustrate how the product can be operated, its potential benefits, and how it fits into a larger system or environment.
The model is particularly useful in the context of online stores and businesses dealing with large collections of product images. By leveraging its power, businesses can streamline their digital asset management workflows, organize image libraries, and deliver personalized user experiences.
Some typical use cases include:
Image library organization - Online stores often have vast libraries of product images that need to be effectively organized and categorized. The classifier can automatically sort images based on their content, facilitating easy retrieval and management.
Search enhancement - The model allows for more accurate search and filtering capabilities. Users can easily find images that showcase the product itself or images that demonstrate its usage in real-world contexts.
Content Curation - The classifier aids in curating image galleries and content showcases by providing an automated classification of images. It aids in showcasing product images in relevant contexts, ensuring a coherent and focused presentation.
Product Recommendations - By understanding whether an image focuses on the product or its application, businesses can enhance their recommendation algorithms. The classifier can be used to tailor recommendations based on the user's preference for pure product images or application images.
An up-to-date reference with all API endpoints is available here:
An ML model that identifies sport activities depicted in images, empowering diverse industries with precise content organization and targeted applications
The Sport classifier is a state-of-the-art artificial intelligence service. Its primary function is to analyze and classify images based on the sports activities they depict. Utilizing cutting-edge deep learning techniques and neural networks, this model can accurately identify various sports and provide valuable insights for content organization and a wide range of applications.
The model offers a diverse set of sports categories, including but not limited to football, basketball, tennis, swimming, gymnastics, baseball and many more. It ensures precise tagging and categorization of sports-related images, making it an indispensable tool for media agencies and content managers.
Sports classification can be applied in multiple domains, including:
Sports media coverage - By quickly identifying the sport depicted in an image, journalists and editors can efficiently organize their image archives and provide real-time coverage during sporting events, enhancing storytelling and enriching their publications.
Sports analytics - The model can aid in automating the process of collecting data by categorizing images from games and training sessions.
Sports product marketing - Sports brands and retailers can utilize the model to optimize their marketing efforts. Sorting images of athletes engaging in specific sports can aid in the creation of targeted campaigns.
Search enhancement - Precise sport categorization can optimize the process of searching and filtering assets, enabling users to find relevant images for their projects with ease.
An up-to-date reference with all API endpoints is available here:
An ML model that accurately classifies images, offering appropriate scene categories and attributes
The Scene classifier is a cutting-edge artificial intelligence system integrated into our service. Its primary function is to analyze and classify images based on their environmental context and distinguish between indoor and outdoor scenes. Leveraging the power of deep learning and neural networks, this model can accurately determine the setting of an image and provide insights about various scene categories, that the image has been determined to be part of.
The model offers a rich set of scene categories and attributes to help users understand the context of their images better. Some of the scene categories include landscapes, beaches, mountains, forests, interiors, exteriors, etc. It can also identify specific attributes like natural lighting, artificial lighting, crowded spaces, open spaces, urban settings, rural settings and more.
Scene classification can be appropriate in the following use cases:
Content organization - The Scene classifier plays a vital role in automatically organizing large collections of images. By accurately determining whether an image is taken indoors or outdoors, users can easily filter and group their assets based on environmental context, streamlining content management and retrieval.
Marketing campaigns - Marketing teams can sort and analyze images that are relevant to specific campaigns. For instance, for an outdoor adventure campaign, the system can help select images of landscapes, mountains, and beaches, thus ensuring consistent and fitting visuals.
Search optimization - Scene classification adds a new layer of metadata to the images. This enhances search capabilities, enabling efficient searching for specific scenes or environments.
An up-to-date reference with all API endpoints is available here:
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An ML model that accurately separates foreground subjects from backgrounds, enabling easy and efficient generation of transparent assets
The Background remover is an advanced machine-learning model, designed to make background removal an effortless and time-saving process. By leveraging the power of deep learning algorithms, it can accurately separate foreground subjects from their backgrounds, resulting in high-quality transparent assets.
The model is built on a foundation of state-of-the-art deep learning techniques, particularly focused on semantic segmentation. It is trained on extensive datasets containing a diverse range of images, ensuring robustness and adaptability to handle various edge cases and complexities. By utilizing a multi-layered neural network architecture, it processes each pixel in an image to classify it as part of the foreground or background.
The model undergoes a rigorous training process, learning to identify different object shapes, fine details and semi-transparent elements in the images. As a result, it can accurately separate foreground subjects from their backgrounds, even in challenging scenarios.
Automatic background removal can be a game-changer for many workflows. By transforming product photos, portraits and creative artworks into transparent assets in a matter of seconds, it eliminates any fiddling with tedious image editing software or outsourcing the task to graphic designers. Professional-grade background removal can be achieved effortlessly, saving valuable time and resources.
Automatic background removal can prove invaluable for multiple use cases, such as:
E-commerce product catalogs - The model ensures consistent, visually appealing product images that seamlessly blend into any website or marketing material and can streamline any e-commerce business.
Portrait photography - The background remover offers a quick and efficient way to remove distracting backgrounds from portrait shots, enabling better focus on the subject's features and expressions.
Design projects - Designers can explore boundless creative possibilities by easily overlaying graphics, text or new backgrounds, allowing for eye-catching collages, posters, and social media posts.
Presentations and marketing materials - Creation of professional presentations by placing images on any background, ensuring a clean and polished look that captivates the audience.
Image localization - The model facilitates localization for global audiences by enabling easy background replacement to suit different cultural contexts and brand aesthetics.
An up-to-date reference with all API endpoints is available here:
Enhance content management with general-purpose visual and language understanding
Bridging the gap between visual and textual content is a crucial step in unlocking the full potential of digital assets. The Image-to-text ML model is an advanced solution designed to do just that by providing general-purpose visual and language understanding.
The model leverages state-of-the-art natural language processing and computer vision techniques to facilitate the understanding of images and textual data. When a user submits an image and an accompanying textual prompt (typically in the form of a question regarding the image), the model processes the visual and textual data, identifying objects, context and relationships within the image, and generates a relevant response.
Users can pose a wide range of questions, from object recognition and content analysis to more complex queries related to the image. The output is a properly constructed natural language answer that provides insights or information pertaining to the submitted data.
Our Image-to-text functionality is a versatile tool that gives customers the ability to extract insights, enrich content and enhance the overall management of digital assets.
The Image-to-text functionality is powerful enough to be applied across a spectrum of industries and domains, such as:
Content tagging - Customers can automatically generate descriptive metadata for images, simplifying the organization and retrieval of digital assets.
E-commerce and product catalogs - E-commerce platforms can utilize the model to answer user queries about product images, providing detailed information and enhancing the shopping experience.
Media and entertainment - Media companies can analyze and describe scenes, characters and objects in images, aiding in content categorization and analysis.
Educational content - Educational institutions can enhance e-learning platforms by automatically generating explanations and descriptions for visual content in course materials.
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
Precise classification of property-related images across various industries.
Efficient categorization of digital content is essential for organizing and retrieving assets effectively. The Property classification model is an advanced machine learning (ML) solution designed to identify and classify the type of real estate property depicted within an image.
The model analyzes the input image and determines if the displayed realty falls into one of the following property classes:
Apartment
Bathroom
Bedroom
Dining
Garage
House
Industrial
Kitchen
Living
Office
The Property classification feature is based on state-of-the-art computer vision techniques and deep learning algorithms that interpret the image data and identify patterns that are indicative of the property type depicted. This allows the model to accurately predict the predefined category that the image is part of, enabling precise classification. By doing this, this model significantly enhances the management of digital assets, streamlining content organization and retrieval for various industries.
Property classification finds utility in a variety of use cases, all aimed at improving digital asset management and content organization:
Real estate listings - Real estate agencies can use the model to automatically classify and organize property images for listing websites, making it easier for potential buyers or renters to find properties of interest.
Property management - Property management companies can organize images of rental properties, simplifying property management and tenant communication.
Interior design - Interior design platforms can categorize images of furniture and home decoration products, thus enhancing the online shopping experience for customers.
Hospitality industry - The model can help hotels and hospitality businesses sort images of their rooms, facilities and dining areas for marketing and reservation platforms.
E-commerce product classification - E-commerce platforms can use the model to automatically categorize products based on the type of setting they are intended for (e.g., kitchen appliances, bedroom furniture).
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
An ML model that efficiently detects and removes compression artifacts, enhances image quality while preserving vital visual elements
The Artifact remover is specifically designed to detect and eliminate a wide array of artifacts, primarily caused by heavy lossy compression, ensuring a remarkable enhancement of image quality and the preservation of essential visual elements.
The model is based on deep learning techniques that analyze and learn from vast and diverse datasets of images featuring various compression artifacts. Through this extensive training, the model becomes proficient in recognizing specific patterns and distortions linked to aggressive compression methods.
Once it receives an image, the model efficiently identifies the presence of compression artifacts and applies sophisticated image restoration algorithms to remove or reduce them. The restoration process restores crucial details, textures, and sharpness, resulting in an image with heightened clarity and visual appeal.
Automatic artifact removal and quality improvement find valuable applications in various domains:
E-commerce platforms - In the world of online retail, image quality plays a crucial role in customer engagement and purchasing decisions. The model ensures that product images are of top-notch quality by removing compression artifacts, thus improving the overall shopping experience by delivering visually appealing product showcases.
Digital advertising - High-quality visuals are essential for successful digital advertising campaigns. Captivating ad campaigns with artifact-free images can boost engagement and strengthen the brand message.
Archives and galleries - Historical archives and art galleries often house valuable images that may have undergone degradation due to outdated compression techniques. Restoring such images ensures the preservation of their visual authenticity.
Printing and publishing - In print media image quality is crucial. By employing the model to remove compression artifacts, publishers can achieve clear, vivid images that resonate with readers and convey their intended message effectively.
An up-to-date reference with all API endpoints is available here:
Automatic and accurate blurring of license plate numbers in images to protect privacy and comply with data regulations
License plate blurring is the process of obscuring the license plates of vehicles in images so they become unreadable. Such a feature can be really useful, especially as online privacy is becoming a concern for many. It can be used to prevent the identification of vehicles by automatically blurring their license plates.
The Number plate anonymizer is an advanced ML model, designed to protect privacy and comply with data protection regulations. It efficiently detects vehicle registration plates within images and automatically applies a precise blur filter, rendering the plate numbers and characters illegible, while preserving the integrity of the surrounding content.
Automatic plate blurring can be useful across various scenarios:
Security - The model can be utilized to automatically blur license plates in user images, protecting the privacy of individuals and vehicles by ensuring that sensitive information remains confidential.
Public image galleries - Any visible license plates are automatically blurred, adhering to privacy regulations and protecting personal data.
Vehicle sales and auctions - In online vehicle sales or auction platforms, license plates in vehicle images can be blurred, thus safeguarding the identities of sellers and buyers.
Insurance and claims - Insurance companies can anonymize sensitive information on damaged vehicles in images used for claims processing.
An up-to-date reference with all API endpoints is available here:
Advanced deep learning techniques that generate high-quality and realistic images based on provided text prompts
The Text-to-image generator is a cutting-edge ML model, designed to generate captivating and realistic images based on provided text prompts. It allows users to bring their textual ideas and concepts to life, creating visually stunning assets that complement their digital content library.
To synthesize images from text descriptions, the model first processes the textual input, understanding the context, objects and settings described. Then, it generates high-resolution, visually coherent images that capture the essence of the text prompt.
The model facilitates image creation by empowering users to generate stunning visuals effortlessly. The image synthesis capabilities allow the generator to unlock limitless creative possibilities, making it a valuable tool.
Tex-to-image generation can be a game changer in several use cases, including:
Marketing and advertising - Marketers can visualize and prototype advertising campaigns. By converting taglines into images, they can create compelling visuals that resonate with the target audience, boosting engagement and brand recall.
Product prototyping - Product development teams can visualize concepts and design ideas. By describing product features in text, they can rapidly generate images that represent potential product variations and iterate through designs effectively.
Educational content - Textual descriptions of historical events, scientific concepts or literary settings can be transformed into visually engaging images that enhance the experience for users of e-learning platforms.
Content creation - By providing textual prompts for abstract ideas, designers, artists and content creators can generate unique and imaginative visuals that can be used in digital art, illustrations or graphic designs.
An up-to-date reference with all API endpoints is available here:
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An ML model that automatically counts the number of human faces in an image
Images play a crucial role in various industries, from advertising and marketing to security and analytics. Counting the number of visible human faces in them is a fundamental task for multiple applications.
Manual face counting can be time-consuming and prone to errors. Therefore, our face counting ML model is designed to streamline this process by automating the detection and counting of human faces, providing businesses with valuable insights.
The model is an advanced machine learning algorithm integrated into our service. It automatically analyzes the pixel content of images, extracts their features, and accurately detects and counts the number of human faces present.
Automatic face counting can be valuable in multiple situations, including:
Crowd management - The model assists in crowd management by automatically counting the number of people in specific areas or events. This information can lead to better resource allocation and crowd flow optimization.
Audience measurement - Counting the number of faces in promotional materials enables businesses to analyze audience engagement, measure campaign effectiveness and make data-driven decisions.
Social media analytics - The model can be utilized to quantify the reach and impact of visual content. By counting faces in shared images, businesses can gauge audience interaction, identify trends, and measure the virality of content.
Retail analytics - By counting the number of faces in-store or at specific displays, retailers can gauge customer engagement, optimize store layouts, and measure the effectiveness of merchandising strategies.
An up-to-date reference with all API endpoints is available here:
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A ML model that provides a reliable means of verifying the authenticity of property images for real estate websites with user-generated content.
When it comes to Digital Asset Management (DAM) services, the veracity and trustworthiness of digital content are paramount, especially in the real estate sector. The Real estate authenticity verification model is an innovative machine learning solution designed to classify real estate images into two distinct categories:
Real images - Authentic images captured via phones and cameras that provide genuine representations of real estate properties.
Artificial images - Images that are synthetically crafted using specialized software and provide visually appealing but artificial and unnatural portrayals of properties.
This model can play a pivotal role in authenticating the legitimacy of property images, making it an ideal tool for enhancing real estate websites that utilize user-generated content. Authenticity verification can not only improve user trust but also streamline the management of real estate digital assets.
Real estate authenticity verification can be employed in a range of scenarios:
Real estate listings - Online marketplaces for buying and selling properties can use the model to verify the authenticity of user-submitted property images, enhancing the credibility of their listings and reducing the likelihood of fraudulent ones.
Vacation rental - Platforms for vacation rentals can ensure that images accurately represent the properties they advertise, providing peace of mind to travelers.
Property management - Property management companies can authenticate images provided by tenants or property owners, aiding in the transparent documentation of property conditions.
Property valuation - Real estate valuation services can use the model to confirm the authenticity of images when assessing property values.
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
Machine learning models that analyze the content of an image and assign it to predefined categories or labels
Machine learning models, particularly classification models, play a crucial role in digital asset management. Classification models are algorithms that learn patterns and relationships in data to assign predefined categories or labels to new, unseen data. They can be used to categorize and organize assets, such as images, videos, or documents, based on their content or characteristics.
Classification models are typically trained on labeled datasets, where each data instance is associated with a known category or label. During the training process, the model learns to recognize patterns and features in the input data that differentiate one category from another. Once trained, the model can classify new, unseen data based on the learned patterns.
The following pages describe the models that are available in ASK Filerobot:
A machine-learning algorithm that utilizes optical character recognition techniques to accurately identify and extract text from images or scanned documents
The OCR ML model is an integral component of our service, providing robust optical character recognition capabilities with support for multiple languages.
The model is designed to convert text present in images or scanned documents into editable and searchable data. It leverages the power of machine learning techniques to accurately recognize characters and words, enabling efficient text extraction and analysis.
It examines the input, tries to detect any text fragments present in the image and recognizes the characters in those fragments according to the specified language. Detected fragments with a good enough confidence level are returned as text strings.
The OCR service can be useful for multiple use cases, including:
Text extraction and indexing - The model extracts text from images or scanned documents, enabling efficient indexing and searching of digital assets. Users can find images or documents based on specific keywords or phrases mentioned within the text content.
Document digitization - Important information can be preserved by converting physical documents into digital formats.
Language translation - Combined with language translation capabilities, OCR can facilitate multilingual asset management, enabling users to search and translate text in various languages.
Improved accessibility - Converting text within images or scanned documents into machine-readable format enhances accessibility for visually impaired individuals, enabling screen readers or assistive technologies to interpret the content.
An up-to-date reference with all API endpoints is available here:
An ML model that detects and recognizes vehicle registration plate numbers
The Number plate recognizer is a machine-learning model that detects and recognizes vehicle registration plate numbers in images.
It is developed and trained to detect vehicle license plates, recognize the characters on the plate, and return them as text strings. The model takes in an input image containing a vehicle and outputs the alphanumeric number present on the vehicle's registration plate in text form.
The process of license plate recognition consists of the following steps:
License plate detection;
Pre-processing the resulting image from Step 1 (warping, deskewing) to prepare it for optical character recognition (OCR);
Passing the image from Step 2 through an OCR engine and receiving the recognized characters.
Some typical use cases for this model include:
Efficient vehicle cataloging - Vehicles can automatically be cataloged in a database. When new vehicle images are uploaded, the model can extract the registration plate numbers and associate them with the corresponding vehicle records, allowing for quick and accurate identification and retrieval of vehicle assets.
Smart searching - Users can leverage plate number recognition to perform targeted searches and easily locate specific vehicles, thus saving time and effort.
Automated metadata - Vehicle assets can be tagged with relevant metadata based on their registration plate numbers. This metadata may include vehicle make, model, year, and other information associated with the recognized plate.
Regulatory compliance - The model can aid in ensuring compliance with legal requirements related to vehicle registration and documentation.
An up-to-date reference with all API endpoints is available here:
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Automatic assignment of relevant tags and keywords to the input image
An ML model that detects logos of popular brands visible in the input image
An ML model that extracts the most prominent colors from an image
A combination of ML models that accurately detect human faces and predict crucial characteristics like facial landmarks, expression, ethnicity, age and gender
A system that detects human faces, extracts feature vectors and clusters similar faces to efficiently group images based on the individuals present in them
An ML model that detects and recognizes vehicle registration plate numbers
A machine learning algorithm that utilizes optical character recognition techniques to accurately identify and extract text from images or scanned documents
An ML model classifying an image as pure product image or application image, showing the product in use
An ML model that accurately classifies images, offering appropriate scene categories and attributes
An ML model that identifies sport activities depicted in images, empowering diverse industries with precise content organization and targeted applications
Automation of content analysis for appropriateness, facilitating efficient content moderation
Content moderation models are machine learning algorithms designed to analyze and assess various types of digital content and determine its appropriateness based on predefined guidelines or criteria. These models play a crucial role in moderating user-generated content, ensuring compliance with community standards, guidelines or legal requirements.
Their primary goal is to automate the process of content moderation, which can be time-consuming and challenging to handle manually. They employ a combination of natural language processing (NLP), computer vision and audio analysis techniques to analyze and classify content based on multiple factors such as explicit or harmful language, hate speech, offensive imagery, violence, nudity or other criteria defined by the platform or organization.
The training of moderation ML models involves feeding them with large labeled datasets. These datasets serve as examples to teach the model to recognize and classify different types of inappropriate or undesirable content. Through the training process, the model learns patterns, features, and context cues that help it make accurate predictions about the suitability or moderation level of new, unseen content.
By automating the initial content moderation process, they can significantly reduce the manual workload, increase efficiency and provide consistent enforcement of content policies.
An ML model for adult content detection
The NSFW machine-learning model is an advanced algorithm designed to accurately detect and classify adult content within digital media files. It plays a vital role in ensuring the safe and appropriate use of media assets in various industries.
By employing state-of-the-art computer vision techniques, the model offers powerful capabilities to automatically identify explicit and adult content, enhancing content moderation and compliance processes.
The model utilizes deep learning algorithms trained on extensive datasets to accurately identify and classify adult content, including nudity, explicit imagery and suggestive or provocative materials. It is seamlessly integrated into our service, allowing for efficient and scalable content analysis.
Possible applications of adult content detection include:
Content moderation - The model can automatically filter and moderate user-generated content. It helps prevent the upload of adult or explicit materials, ensuring a safer online environment.
Brand protection - Advertisers can safeguard their reputations and protect their digital assets. By automatically screening media content before publishing or distribution, companies can maintain brand integrity and avoid association with inappropriate or NSFW materials.
Legal compliance - In industries such as publishing, advertising, and e-commerce, the NSFW model can help ensure compliance with legal and regulatory standards. By identifying and flagging adult content, companies can adhere to age restrictions, protect minors from explicit material and avoid legal repercussions.
Content Curation - Content creators can efficiently curate their collections. The automatic tagging and categorization process for assets makes it easier to search and manage content based on its appropriateness for different target audiences.
An up-to-date reference with all API endpoints is available here:
A ML model to identify the presence of visual watermarks in input images
Protection of intellectual property and brand integrity is of great importance to content creators, marketers and organizations. Watermarks, which are often applied to images to prevent unauthorized use, play a significant role in safeguarding digital assets.
The Watermark detection functionality has been developed as a crucial component of our service. This machine learning model is designed to accurately determine whether an input image contains a visual watermark, thus providing customers with the ability to identify and work with their digital assets more effectively.
The employed deep learning algorithms analyze the input image data, extracting unique features and patterns that are indicative of visible watermarks, with the end goal being distinguishing between watermarked and non-watermarked images with high accuracy.
Watermark detection can be employed in a variety of use cases, including:
Licensing compliance - Media agencies can use the feature to ensure that content consumers adhere to licensing agreements, thus preventing unauthorized use of their assets.
Asset management - The model can automatically categorize and tag images, allowing users to quickly locate and retrieve non-watermarked versions of their assets.
Quality control - Design and marketing teams can easily verify that images in promotional materials are free from unintended watermarks, ensuring consistent branding.
Information about the specific API endpoints is available in an always up-to-date documentation, that can be accessed via the following link:
There, you can find detailed information about the API endpoints, together with all required request parameters, so you know how to interact with them.
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