Validating Model Performance Using Encord Active

Machine learning and artificial intelligence algorithms are constantly evolving to solve complex problems and enhance our understanding of data. One interesting group of models is diffusion models, which have gained significant attention for their ability to capture and simulate complex processes like data generation and image synthesis. In this article, we will explore: What is diffusion? What are diffusion models? How do diffusion models work? Applications of diffusion models Popular diffusion models for image generation What is Diffusion? Diffusion is a fundamental natural phenomenon observed in various systems, including physics, chemistry, and biology.  This is readily noticeable in everyday life. Consider the example of spraying perfume. Initially, the perfume molecules are densely concentrated near the point of spraying. As time passes, the molecules disperse.  Diffusion is the process of particles, information, or energy moving from an area of high concentration to an area of lower concentration. This happens because systems tend to reach equilibrium, where concentrations become uniform throughout the system. In machine learning and data generation, diffusion refers to a specific approach for generating data using a stochastic process similar to a Markov chain. In this context, diffusion models create new data samples by starting with simple, easily generated data and gradually transforming it into more complex and realistic data. What are Diffusion Models in Machine Learning? Diffusion models are generative, meaning they generate new data based on the data they are trained on.  For example, a diffusion model trained on a collection of human faces can generate new and realistic human faces with various features and expressions, even if those specific faces were not present in the original training dataset. These models focus on modeling the step-by-step evolution of data distribution from a simple starting point to a more complex distribution. The underlying concept of diffusion models is to transform a simple and easily samplable distribution, typically a Gaussian distribution, into a more complex data distribution of interest through a series of invertible operations. Once the model learns the transformation process, it can generate new samples by starting from a point in the simple distribution and gradually "diffusing" it to the desired complex data distribution. Denoising Diffusion Probabilistic Models (DDPMs) DDPMs are a type of diffusion model used for probabilistic data generation. As mentioned earlier, diffusion models generate data by applying transformations to random noise. DDPMs, in particular, operate by simulating a diffusion process that transforms noisy data into clean data samples. Training DDPMs entails acquiring knowledge of the diffusion process’s parameters, effectively capturing the relationship between clean and noisy data during each transformation step. During inference (generation), DDPMs start with noisy data (e.g., noisy images) and iteratively apply the learned transformations in reverse to obtain denoised and realistic data samples. Diffusion Models: A Comprehensive Survey of Methods and Applications DDPMs are particularly effective for image-denoising tasks. They can effectively remove noise from corrupted images and produce visually appealing denoised versions. Moreover, DDPMs can also be used for image inpainting and super-resolution, among other applications. Score-Based Generative Models (SGMs) Score-Based Generative Models are a class of generative models that use the score function to estimate the likelihood of data samples. The score function, also known as the gradient of the log-likelihood with respect to the data, provides essential information about the local structure of the data distribution. SGMs use the score function to estimate the data's probability density at any given point. This allows them to effectively model complex and high-dimensional data distributions. Although the score function can be computed analytically for some probability distributions, it is often estimated using automatic differentiation and neural networks. Score-Based Generative Modeling with Critically-Damped Langevin Diffusion Using the score function, SGMs can generate data samples that resemble the training data distribution. Iteratively updating them toward the log-likelihoods negative gradient achieves this. Stochastic Differential Equations (Score SDEs) Stochastic Differential Equations (SDEs) are mathematical equations describing how a system changes over time when subject to deterministic and random forces. In generative modeling, Score SDEs can parameterize the score-based models. In Score SDEs, the score function is a solution to a stochastic differential equation. The model can learn a data-driven score function that adapts to the data distribution by solving this differential equation. In essence, Score SDEs use stochastic processes to model the evolution of data samples and guide the generative process toward generating high-quality data samples. Solving a reverse-time SDE yields a score-based generative model. Score-Based Generative Modeling through Stochastic Differential Equations Score SDEs and score-based modeling can be combined to create powerful generative models capable of handling complex data distributions and generating diverse and realistic samples. How do Diffusion Models Work? Diffusion models are generative models that simulate data generation using the "reverse diffusion" concept. Let's break down how diffusion models work step-by-step: Data Preprocessing The initial step involves preprocessing the data to ensure proper scaling and centering. Typically, standardization is applied to convert the data into a distribution with a mean of zero and a variance of one. This prepares the data for subsequent transformations during the diffusion process, enabling the diffusion models to effectively handle noisy images and generate high-quality samples. Forward Diffusion During forward diffusion, the model starts with a sample from a simple distribution, typically a Gaussian distribution, and applies a sequence of invertible transformations to "diffuse" the sample step-by-step until it reaches the desired complex data points distribution. Each diffusion step introduces more complexity to the data, capturing the intricate patterns and details of the original distribution. This process can be thought of as gradually adding Gaussian noise to the initial sample, generating diverse and realistic samples as the diffusion process unfolds. Training the Model Training a diffusion model involves learning the parameters of the invertible transformations and other model components. This process typically involves optimizing a loss function, which evaluates how effectively the model can transform samples from a simple distribution into ones that closely resemble the complex data distribution.  Diffusion models are often called score-based models because they learn by estimating the score function (gradient of the log-likelihood) of the data distribution with respect to the input data points. The training process can be computationally intensive, but advances in optimization algorithms, and hardware acceleration have made it feasible to train diffusion models on various datasets. Reverse Diffusion Once the forward diffusion process generates a sample from the complex data distribution, the reverse diffusion process maps it back to the simple distribution through a sequence of inverse transformations. Through this reverse diffusion process, diffusion models can generate new data samples by starting from a point in the simple distribution and diffusing it step-by-step to the desired complex data distribution. The generated samples resemble the original data distribution, making diffusion models a powerful tool for image synthesis, data completion, and denoising tasks. Benefits of Using Diffusion Models Diffusion models offer advantages over traditional generative models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders). These benefits stem from their unique approach to data generation and reverse diffusion.  Image Quality and Coherence Diffusion models are adept at generating high-quality images with fine details and realistic textures. When reverse diffusion is used to examine the underlying complexity of the data distribution, diffusion models make images that are more coherent and have fewer artifacts than traditional generative models. OpenAI's paper, Diffusion Models Beat GANs on Image Synthesis shows that diffusion models can achieve image sample quality superior to current state-of-the-art generative models. Stable Training Training diffusion models are generally more stable than training GANs, which are notoriously challenging. GANs require balancing the learning rates of the generator and discriminator networks, and mode collapse can occur when the generator fails to capture all aspects of the data distribution. In contrast, diffusion models use likelihood-based training, which tends to be more stable and avoids mode collapse. Privacy-Preserving Data Generation Diffusion models are suitable for applications in which data privacy is a concern. Since the model is based on invertible transformations, it is possible to generate synthetic data samples without exposing the underlying private information of the original data.  Handling Missing Data Diffusion models can handle missing data during the generation process. Since reverse diffusion can work with incomplete data samples, the model can generate coherent samples even when parts of the input data are missing. Robustness to Overfitting Traditional generative models like GANs can be prone to overfitting, in which the model memorizes the training data and fails to generalize well to unseen data. Because they use likelihood-based training and the way reverse diffusion works, diffusion models are better at handling overfitting. This is because they create samples that are more consistent and varied. Interpretable Latent Space Diffusion models often have a more interpretable latent space than GANs. The model can capture additional variations and generate diverse samples by introducing a latent variable into the reverse diffusion process. The reverse diffusion process turns the complicated data distribution into a simple distribution. This lets the latent space show the data's important features, patterns, and latent variables. This interpretability, coupled with the flexibility of the latent variable, can be valuable for understanding the learned representations, gaining insights into the data, and enabling fine-grained control over image generation. Scalability to High-Dimensional Data Diffusion models have demonstrated promising scalability to high-dimensional data, such as images with large resolutions. The step-by-step diffusion process allows the model to efficiently generate complex data distributions without being overwhelmed by the data's high dimensionality. Applications of Diffusion Models Diffusion models have shown promise in various applications across domains due to their ability to model complex data distributions and generate high-quality samples. Let’s dive into some notable applications of diffusion models: Text to Video Make-A-Video: Text-to-Video Generation without Text-Video Data. Diffusion models are a promising approach for text-to-video synthesis. The process involves first representing the textual descriptions and video data in a suitable format, such as word embeddings or transformer-based language models for text and video frames in a sequence format. During the forward diffusion process, the model takes the encoded text representation and gradually generates video frames step-by-step, incorporating the semantics and dynamics of the text. Each diffusion step refines the rendered frames, transforming them from random noise into visually meaningful content that aligns with the text. The reverse diffusion process then maps the generated video frames back to the simple distribution, completing the text-to-video synthesis. This conditional generation enables diffusion models to create visually compelling videos based on textual prompts. It has potential applications in video captioning, storytelling, and creative content generation. However, challenges remain, including ensuring temporal coherence between frames, handling long-range dependencies in text, and improving scalability for complex video sequences. Meta's Make-A-Video is a well-known example of leveraging diffusion models to develop machine learning models for text-to-video synthesis. Image to Image Diffusion models offer a powerful approach for image-to-image translation tasks, which involve transforming images from one domain to another while preserving semantic information and visual coherence. The process involves conditioning the diffusion model on a source image and using reverse diffusion to generate a corresponding target image representing a transformed source version. To achieve this, the source and target images are represented in a suitable format for the model, such as pixel values or embeddings. During the forward diffusion process, the model gradually transforms the source image, capturing the desired changes or attributes specified by the target domain. This often involves upsampling the source image to match the resolution of the target domain and refining the generated image step-by-step to produce high-quality and coherent results. The reverse diffusion process then maps the generated target image back to the simple distribution, completing the image-to-image translation. This conditional generation allows diffusion models to excel in tasks like image colorization, style transfer, and image-to-sketch conversion.  The paper Denoising Diffusion Probabilistic Models (DDPM), which was initialized by Sohl-Dickstein et al. and then proposed by Ho et al. 2020 is an influential paper that showcases diffusion models as a potent neural network-based method for image generation tasks. Image Search Diffusion models are powerful content-based image retrieval techniques that can be applied to image search tasks. Using the reverse diffusion process, the first step in using diffusion models for image search is to encode the images in a latent space. During reverse diffusion, the model maps each image to a point in the simple distribution. This latent representation retains the essential visual information of the image while discarding irrelevant noise and details, making it suitable for efficient and effective similarity searches. When a query image is given for image search, the model encodes the query image into the same latent space using the reverse diffusion process. The similarity between the query and database images can be measured using standard distance metrics (e.g., Euclidean distance) in the latent space. Images with the most similar latent representations are retrieved, producing relevant and visually similar images to the query. This application of diffusion models for image search enables accurate and fast content-based retrieval, which is useful in various domains such as image libraries, image databases, and reverse image search engines. Diffusion models are one such model that powers the semantic search feature within Encord Active. When you log into Encord → Active → Choose a Project → Use the Natural Language or Image Similarity Search feature. Here is a way to search with image similarity as the query image: Image Similarity Search within Encord Active. Read the full guide, ‘How to Use Semantic Search to Curate Images of Products with Encord Active,' in this blog post. Reverse Image Search Diffusion models can be harnessed for reverse image search, also known as content-based image retrieval, to find the source or visually similar images based on a given query image.  To facilitate reverse image search with diffusion models, a database of images needs to be preprocessed by encoding each image into a latent space using the reverse diffusion process. This latent representation captures each image's essential visual characteristics, allowing for efficient and accurate retrieval. When a query image is provided for reverse image search, the model encodes it into the same latent space using reverse diffusion. By measuring the similarity between the query image's latent representation and the database images' latent representations using distance metrics (e.g., Euclidean distance), the model can identify and retrieve the most visually similar images from the database. This application of diffusion models for reverse image search facilitates fast and reliable content-based retrieval, making it valuable for various applications, including image recognition, plagiarism detection, and multimedia databases.  Well-known Diffusion Models for Image Generation Stable Diffusion High-Resolution Image Synthesis with Latent Diffusion Models Stable diffusion is a popular approach for image generation that uses diffusion models (DMs) and the efficiency of latent space representation. The method introduces a two-stage training process to enable high-quality image synthesis while overcoming the computational challenges associated directly operating in pixel space. In the first stage, an autoencoder is trained to compress the image data into a lower-dimensional latent space that maintains perceptual equivalence with the original data. This learned latent space is an efficient and scalable alternative to the pixel space, providing better spatial dimensionality scaling properties. By training diffusion models in this latent space, known as Latent Diffusion Models (LDMs), Stable Diffusion achieves a near-optimal balance between complexity reduction and detail preservation, significantly boosting visual fidelity. High-Resolution Image Synthesis with Latent Diffusion Models Stable diffusion introduces cross-attention layers into the model architecture, enabling the diffusion models to become robust and flexible generators for various conditioning inputs, such as text or bounding boxes. This architectural enhancement opens up new possibilities for image synthesis and allows for high-resolution generation in a convolutional manner. The approach of stable diffusion has demonstrated remarkable success in image inpainting, class-conditional image synthesis, text-to-image synthesis, unconditional image generation, and super-resolution tasks. Moreover, it achieves state-of-the-art results while considerably reducing the computational requirements compared to traditional pixel-based diffusion models. The code for stable diffusion has been made publicly available on GitHub. DALL-E 2 Hierarchical Text-Conditional Image Generation with CLIP Latents DALL-E 2 utilizes contrastive models like CLIP to learn robust image representations that capture semantics and style. It has a 2-stage model consisting of a prior stage that generates a CLIP image embedding based on a given text caption and a decoder stage. The model's decoders use diffusion. These models are conditioned on image representations and produce variations of an image that preserve its semantics and style while altering non-essential details. Hierarchical Text-Conditional Image Generation with CLIP Latents The CLIP joint embedding space allows language-guided image manipulations to happen in a zero-shot way. This allows the diffusion model to create images based on textual descriptions without direct supervision. 🔥 NEW RELEASE: We released TTI-Eval (text-to-image evaluation), an open-source library for evaluating zero-shot classification models like CLIP and domain-specific ones like BioCLIP against your (or HF) datasets to estimate how well the model will perform. Get started with it on GitHub, and do ⭐️ the repo if it's awesome. 🔥 Imagen Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding Imagen is a text-to-image diffusion model that stands out for its exceptional image generation capabilities. The model is built upon two key components: large pre-trained frozen text encoders and diffusion models. Leveraging the strength of transformer-based language models, such as T5, Imagen showcases remarkable proficiency in understanding textual descriptions and effectively encoding them for image synthesis. Imagen uses a new thresholding sampler, which enables the use of very large classifier-free guidance weights. This enhancement further enhances guidance and control over image generation, improving photorealism and image-text alignment. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding The researchers introduce a novel, Efficient U-Net architecture to address computational efficiency. This architecture is optimized for better computing and memory efficiency, leading to faster convergence during training. U-Net: Convolutional Networks for Biomedical Image Segmentation A significant research finding is the importance of scaling the pre-trained text encoder size for the image generation task. Increasing the size of the language model in Imagen substantially positively impacts both the fidelity of generated samples and the alignment between images and corresponding text descriptions. This highlights the effectiveness of large language models (LLMs) in encoding meaningful representations of text, which significantly influences the quality of the generated images. The PyTorch implementation of Imagen can be found on GitHub. GLIDE Guided Language to Image Diffusion for Generation and Editing (GLIDE) is another powerful text-conditional image synthesis model by OpenAI. It is a computer vision model based on diffusion models. GLIDE leverages a 3.5 billion-parameter diffusion model with a text encoder to condition natural language descriptions. The primary goal of GLIDE is to generate high-quality images based on textual prompts while offering editing capabilities to improve model samples for complex prompts. GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Models In the context of text-to-image synthesis, GLIDE explores two different guidance strategies: CLIP guidance and classifier-free guidance. Through human and automated evaluations, the researchers discovered that classifier-free guidance yields higher-quality images than the alternative. This guidance mechanism allows GLIDE to generate photorealistic samples that closely align with the text descriptions. One notable application of GLIDE in computer vision is its potential to significantly reduce the effort required to create disinformation or Deepfakes. However, to address ethical concerns and safeguard against potential misuse, the researchers have released a smaller diffusion model and a noisy CLIP model trained on filtered datasets.  OpenAI has made the codebase for the small, filtered data GLIDE model publicly available on GitHub. Diffusion Models: Key Takeaways Diffusion models are generative models that simulate how data is made by using a series of invertible operations to change a simple starting distribution into the desired complex distribution. Compared to traditional generative models, diffusion models have better image quality, interpretable latent space, and robustness to overfitting. Diffusion models have diverse applications across several domains, such as text-to-video synthesis, image-to-image translation, image search, and reverse image search. Diffusion models excel at generating realistic and coherent content based on textual prompts and efficiently handling image transformations and retrievals. Popular models include Stable Diffusion, DALL-E 2, and Imagen.

With increasing reliance on computer vision (CV) systems in multiple industrial domains, the demand for robust data annotation solutions is rising exponentially. The most recent reports project the data annotation tools market to have a compound annual growth rate (CAGR) of 21.8% from 2024 to 2032. However, as several companies emerge offering annotation platforms and services, finding a cost-effective provider is challenging. While many platforms offer advanced annotation features, only a few meet the scalability and security requirements essential for enterprise-level CV applications. This article discusses the ten best video and image annotation companies in 2024 to help you with your search. The following lists the companies we think are driving the data annotation space: Encord iMerit Appen Label Your Data KeyMakr TrainingData SuperbAI Kili Technology Telus International SuperAnnotate CogitoTech LabelBox Top 12 Data Annotation and Data Labeling Companies Data annotation companies offering labeling solutions must meet stringent security and scalability requirements to match the high standards of the modern artificial intelligence (AI) space. Below are the twelve top companies, ranked based on the following factors:  Data security protocols: Compliance with data security regulations and use of encryption algorithms. Scalability: The solution’s ability to handle large data volumes and variety. Collaboration: Tools allowing different team members to collaborate on projects. Ease of use: A user-friendly interface that is intuitive and easy to navigate. Supported data types: support for different modalities such as video, image, audio, and text. Automation: AI-based labeling for speeding up annotation processes. Other functionalities for streamlining the annotation workflow include integration with cloud services and advanced annotation methods for complex scenarios. Let’s explore each company's annotation platforms or services and see the key features based on the above factors to help you determine the most suitable option. Encord Encord is an end-to-end data platform that enables you to annotate, curate, and manage computer vision datasets through AI-assisted annotation features. It also provides intuitive dashboards to view insights on key metrics, such as label quality and annotator performance, to optimize workforce efficiency and ensure you build production-ready models faster. SOTA Model-assisted Labeling and Customizable Workflows with Encord Annotate Key Features Data security: Encord complies with the General Data Protection Regulation (GDPR), System and Organization Controls 2 (SOC 2), and Health Insurance Portability and Accountability Act (HIPAA) standards. It uses advanced encryption protocols to ensure data security and privacy. Scalability: The platform allows you to upload up to 500,000 images (recommended), 100 GB in size, and 5 million labels per project. You can also upload up to 200,000 frames per video (2 hours at 30 frames per second) for each project. See more guidelines for scalability in the documentation. Collaboration: You can create workflows and assign roles to relevant team members to manage tasks at different stages. User roles include admin, team member, reviewer, and annotator. Ease-of-use: Encord Annotate offers an intuitive user interface (UI) and an SDK to label and manage annotation projects. Supported data types: The platform lets you annotate images, videos (and image sequences), DICOM, and Mammography data. Supported annotation methods: Encord supports multiple annotation methods, including classification, bounding box, keypoint, polylines, and polygons. Automated labeling: The platform speeds up the annotation with automation features, including: - Segment Anything Model (SAM) to automatically create labels around distinct features in all supported file formats. - Interpolation to auto-create instance labels by estimating where labels should be created in videos and image sequences. - Object tracking to follow entities within images based on pixel information enclosed within the label boundary. Integration: Integrate popular cloud storage platforms, such as AWS, Google Cloud, Azure, and Open Telekom Cloud OSS, to import datasets. Best for Teams looking for an enterprise-grade image and video annotation solution to produce high-quality data for computer vision models. Pricing Encord has a pay-per-user pricing model with Starter, Team, and Enterprise options. Learn more about automated data annotation by reading our guide to automated data annotation. iMerit iMerit offers Ango Hub, a data annotation solution built on a generative AI framework that lets you build use-case-specific applications for autonomous vehicles, agriculture, and healthcare industries. iMerit Key Features Collaboration: The Ango Hub solution lets you add labelers and reviewers to customized workflows for managing annotation projects. Ease-of-use: The platform offers an intuitive UI to label items, requiring no coding expertise. Supported data types: Ango Hub supports audio, image, video, DICOM, text, and markdown data types. Supported labeling methods: The solution supports bounding boxes, polygons, polylines, segmentation, and tools for natural language processing (NLP). Integration: The platform features integrated plugins for automated labeling and machine learning models for AI-assisted annotations. Best for Teams searching for an integrated labeling platform for annotating text, video, and image data. Pricing Pricing information is not publicly available. Contact the team to get a quote. Appen Appen offers data annotation solutions for building large language models (LLMs) by providing a standalone labeling platform and data labeling services through expert linguists. Appen Key Features Workforce capacity: Appen’s managed services include more than a million specialists speaking over 200 languages across 170 countries. With the option to combine its platform with its services, the solution becomes highly scalable. Supported data types: Appen’s platform lets you label documents, images, videos, audio, text, and point-cloud data. Supported annotation methods: Labeling methods include bounding boxes, cuboids, lines, points, polygons, ellipses, segmentation, and classification. Instruction datasets: The company also offers domain-specific instruction datasets for training LLMs. Best for Teams looking for a hybrid solution for building multi-modal models for text and vision applications. Pricing Pricing is not publicly available. Label Your Data Label Your Data is a data annotation service provider offering video and image annotation services for CV and NLP applications. Label Your Data Key Features Data security: The company complies with ISO 27001, GDPR, and CCPA standards. Workforce capacity: Label Your Data builds a remote team of over 500 data annotators to speed up the annotation process. Supported data types: The solution supports image, video, point-cloud, text, and audio data. Supported labeling methods: CV methods include semantic segmentation, bounding boxes, polygons, cuboids, and key points. NLP methods include named entity recognition (NER), sentiment analysis, audio transcription, and text annotation. Best for Teams looking for a secure annotation service provider for completely outsourcing their labeling efforts. Pricing Label Your Data provides on-demand, short- and long-term plans. Keymakr Keymakr is an image and video annotation service provider that manages labeling processes through its in-house professional experts. Keymakr Key Features Labeling capacity: You can label up to 100,000 data items. Supported data types: The platform supports image, video, and point-cloud data. Supported labeling methods: Keymakr offers annotations that include bounding boxes, cuboids, polygons, semantic segmentation, key points, bitmasks, and instance segmentation. Smart assignment: The solution features a smart distribution to match relevant annotators with suitable tasks based on skillset. Performance tracking: Keymakr provides performance analytics to track progress and alert managers in case of issues. Data collection and creation: The company also offers services to create relevant data for your projects or collect it from reliable sources. Best for Beginner-level teams working CV projects, requiring data creation and annotation services. Pricing Pricing is not publicly available. TrainingData TrainingData is a Software-as-a-Service (SaaS) data labeling application for CV projects, featuring pixel-level annotation tools for accurate labeling. TrainingData Key Features Data security: The company provides a Docker image to run on your local network through a secure virtual private network (VPN) connection. Scalability: You can label up to 100,000 images. Collaboration: TrainingData’s platform lets you create projects and add relevant collaborators with suitable roles, including reviewer, annotator, and admin. Supported labeling methods: The platform offers multiple labeling tools, including a brush and eraser for pixel-accurate segmentation, bounding boxes, polygons, key points, and a freehand drawer for freeform contours. Integration: TrainingData integrates with any cloud storage service that complies with cross-origin resource sharing (CORS) policy. Best for Teams looking for an on-premises image annotation platform for segmentation tasks. Pricing TrainingData offers free, pro, and enterprise packages. SuperbAI SuperbAI offers multiple products for building AI models, including a data management platform, a labeling solution, and a tool for training, evaluating, and deploying models. SuperbAI Key Features Data security: SuperbAI complies with SOC standards and encrypts all data using Advanced Encryption Standard - 256 (AES-256). Collaboration: The platform offers access management tools and lets you invite team members as admins, labelers, and managers. Supported data types: SuperbAI supports images and videos in PNG, BMP, JPG, and MP4 formats. It also supports point-cloud data. Supported labeling methods: The solution supports all standard labeling methods, including bounding boxes, polylines, polygons, and cuboids. Integration: The platform integrates with Google Cloud, Azure, AWS, and Slack. Best for Teams looking for an integrated data management solution for training machine learning algorithms. Pricing SuperbAI offers starter and enterprise packages. Kili Technology Kili Technology offers an intuitive labeling platform to annotate data for LLMs, generative AI, and CV models with quality assurance features to produce error-free datasets. Kili Technology Key features Collaboration: The platform lets you assign multiple roles to team members, including reviewer, admin, manager, and labeler, to collaborate on projects through instructions and feedback. Ease-of-use: Kili offers a user-friendly UI for managing workflows, requiring minimal code. Supported labeling methods: The tool supports bounding boxes, optical character recognition (OCR), NERs, pose estimation, and semantic segmentation. Automation: Kili supports automated labeling through active learning and pre-annotations using ChatGPT and SAM. Best for Data scientists looking for a lightweight annotation solution for building generative AI applications. Pricing Pricing depends on the number of items you need to label. Telus International Telus International’s Ground Truth (GT) studio offers three platforms as part of a managed service to build training datasets for ML models. GT Manage helps with people and project management; GT Annotate lets you annotate image and video data. GT Data is a data creation and collection tool supporting multiple data types. Telus International Key Features Data security: GT Annotate complies with SOC 2 standards and implements two-factor authentication with firewall applications and intrusion detection for data security. Collaboration: GT Manage features workforce management tools for optimal task distribution and quality control. Supported data types: You can collect image, video, audio, text, and geo-location data using GT data. Supported labeling methods: GT Annotate supports bounding boxes, cuboids, polylines, and landmarks. Best for Teams looking for a complete AI solution for collecting, labeling, and managing raw data. Pricing Pricing information is not publicly available. SuperAnnotate SuperAnnotate offers a data labeling tool that lets you manage AI data through collaboration tools and annotation workflows while providing quality assurance features to produce labeling accuracy. SuperAnnotate Key Features Collaboration: SuperAnnotate lets you create teams and assign relevant roles such as admin, annotator, and reviewer. Ease-of-use: The platform has an easy-to-use UI. Supported data types: SuperAnnotate supports image, video, text, and audio data. Supported labeling methods: The platform has tools for categorization, segmentation, pose estimation, object tracking, sentiment analysis, and speech recognition. Best for Teams looking for an annotation solution to build generative AI applications. Pricing The platform offers free, pro, and enterprise versions. Cogito Cogito is a data labeling service provider that employs a large pool of human annotators to deliver annotations for generative AI, CV, content moderation, NLP, and data processing. Cognito Key Features Data security: Cogito complies with GDPR, SOC 2, HIPAA, CCPA, and ISO 27001 standards. Supported data types: The platform supports image, video, audio, text, and point-cloud data. Automation: Cogito uses AI-based algorithms to label large data volumes. Best for Startups looking for a company to outsource their AI operations. Pricing Pricing is not publicly available. Labelbox Labelbox offers multiple products for managing AI projects. Its data labeling platform allows you to annotate various data types for building vision and LLM applications. LabelBox Key Features Data security: Labelbox complies with several regulatory standards, including GDPR, CCPA, SOC 2, and ISO 27001. Collaboration: Users can create projects and invite in-house labeling team members with relevant roles to manage the annotation workflow. Ease-of-use: Labelbox has a user-friendly interface with a customizable labeling editor. Automation: The platform supports model-assisted labeling (MAL) to import AI-based classifications for your data. Integrability: Labelbox integrates with AWS, Azure, and Google Cloud to access data repositories quickly. Best for Teams looking for labeling solutions to build applications for e-commerce, healthcare, and financial services industries. Pricing Labelbox offers free, starter, and enterprise versions. Still confused about whether to buy a tool or go for open-source solutions? Read some lessons from practitioners regarding build vs. buy decisions Data Annotation Companies: Key Takeaways CV applications are driving the current industrial landscape by innovating fields like medical imaging, robotics, retail, etc. However, CV’s rapid expansion into these domains calls for robust data annotation tools and services to build high-quality training data. Below are a few key points regarding data annotation companies in 2024. Security is key: With data privacy regulations becoming stricter globally, companies offering annotation solutions must have compliance certifications to ensure data protection. Scalability: Annotation companies should offer scalable tools to handle the ever-increasing data volume and variety. Top annotation companies in 2024: SuperAnnotate, Encord, and Kili are the top 3 companies that provide robust labeling platforms and services.

Finding contextually relevant images from large datasets remains a substantial challenge in computer vision. Traditional search methodologies often struggle to grasp the semantic nuances of user queries because they rely on image metadata. This usually leads to inefficient searches and inaccurate results. If you are working with visual datasets, traditional search methods cause significant workflow inefficiencies and potential data oversight, particularly in critical fields such as healthcare and autonomous vehicle development. How do we move up from searching by metadata? Enter semantic search! It uses a deep understanding of your search intent and contextual meaning to deliver accurate and semantically relevant results. So, how do you implement semantic search? Encord Active is a platform for running semantic search on your dataset. It uses OpenAI’s CLIP (Contrastive Language–Image Pre-Training) under the hood to understand and match the user's intent with contextually relevant images. This approach allows for a more nuanced, intent-driven search process. This guide will teach you how to implement semantic search with Encord Active to curate datasets for upstream or downstream tasks. More specifically, you will curate datasets for annotators to label products for an online fashion retailer. At the end of this guide, you should be able to: Perform semantic search on your images within Encord. Curate datasets to send over for annotation or downstream cleaning. See an increase in search efficiency, reduced manual effort, and more intuitive interaction with your data. What are Semantic Image Search and Embeddings? When building AI applications, semantic search and embeddings are pivotal.  Semantic search uses a deep understanding of user intent and the contextual meanings of queries to deliver search results that are accurate and semantically relevant to the user's needs. Semantic search bridges the gap between the complex, nuanced language of the queries and the language of the images. It interprets queries not as mere strings of keywords but as expressions of concepts and intentions. This approach allows for a more natural and intuitive interaction with image databases. This way, you can find images that closely match your search intent, even when the explicit keywords are not in the image metadata. Image Embeddings to Improve Model Performance Embeddings transform data—whether text, images, or sounds—into a high-dimensional numerical vector, capturing the essence of the data's meaning. For instance, text embeddings convert sentences into vectors that reflect their semantic content, enabling machines to process and understand them. OpenAI's Contrastive Language–Image Pre-Training (CLIP) is good at understanding and interpreting images in the context of natural language descriptions under a few lines of code. CLIP is a powerful semantic search engine, but we speak to teams running large-scale image search systems about how resource-intensive it is to run CLIP on their platform or server. Check out our workshop on building a semantic visual search engine with ChatGPT & CLIP.   Encord Active runs CLIP under the hood to help you perform semantic search at scale. Semantic Search with Encord Active Encord Active uses its built-in CLIP to index your images when integrating them from Annotate. This indexing process involves analyzing the images and textual data to create a searchable representation that aligns images with potential textual queries. You get this and an in–depth analysis of your data quality on an end-to-end data-centric AI platform. You can perform semantic search with Encord Active in two ways: Searching your images with natural language (text-based queries). Searching your images using a reference or anchor image. The walkthrough below uses Encord Annotate to create a project and import the dataset. We use Encord Active to search data with natural language. We recommend you sign up for an Encord account to follow this guide.   Import your Project A good place to start using Active is Importing an Annotate Project. From Annotate you can configure the Dataset, Ontology, and workflow for your Project. Once you complete that, move to Active. Explore your Dataset Before using semantic search in Encord Active, you must thoroughly understand and explore your dataset. Start by taking an inventory of your dataset. Know the volume, variety, and visual content of your images. Understanding the scope and diversity of your dataset is fundamental to effectively using semantic search. One of the most exciting features of Encord Active is that it intuitively shows you all the stats you need to explore the data. Within the Explorer, you can already see the total number of images, probable issues within your dataset, and metrics (for example, “Area”) to explore the images: Play around with the Explorer features to inspect the dataset and understand the visual quality. You can also examine any available metadata associated with your images. Metadata can provide valuable insights into the content, source, and characteristics of your images, which can be instrumental in refining your search. You can also explore your image embeddings by switching from Grid View to Embeddings View in the Explorer tab: It’s time to search smarter 🔎🧠. Search your Images with Natural Language You’ve explored the dataset; you are now ready to curate your fashion products for the listings with semantic searches using natural language queries. For example, if you want images for a marketing campaign themed around "innovation and technology," enter this phrase into the search bar. Encord Active then processes this query, considering the conceptual and contextual meanings behind the terms, and returns relevant images. Within the Explorer, locate the “Natural Search” bar.  There are two ways to perform semantic search within Encord: through natural language or a reference image. We’ll use natural language for the rest of this guide.   Say our online platform does not have enough product listings for white sneakers. What can we do? In this case, enter “white sneakers” in the search bar: Awesome! Now you see most images of models in white sneakers and a few that aren’t. Although the total images in our set are 2,701, imagine if the product listings were an order of magnitude larger. This would be consequential. Before curating the relevant images, let’s see how you can search by similar images to fine-tune your search even further. Search your Images with Similar Images Select one of the images and click the expansion button. Click the “Find similar” button to use the anchor image for the semantic similarity search: Sweet! You just found another way to tailor your search. Next, click the “x” (or cancel) icon to return to the natural language search explorer. It’s time to curate the product images. Curate Dataset in Collections Within the natural language search explorer, select all the visible images of models with white sneakers. One way to quickly comb through the data and ensure you don’t include false positives in your collections is to use the Select Visible option. This option only selects images you can see, and then you can deselect images of models in non-white sneakers. In this case, we will quickly select a few accurate options we can see: On the top right-hand corner, click Add to a Collection, and if you are not there already, navigate to New Collection. Name the Collection and add an appropriate and descriptive comment in the Collection Description box. Click Submit when you: Once you complete curating the images, you can take several actions: Create a new dataset slice. Send the collection to Encord Annotate for labeling. Bulk classify the images for pre-labeling. Export the dataset for downstream tasks. Here, we will send the collection to Annotate to label the product listings. We named it “White Sneakers” and included comments to describe the collection. Send Dataset to Encord Annotate Navigate to the Collections tab and find the new collection (in our case, “White Sneakers”). Hover over the new collection and click the menu on the right to show the list of next action options. Click Send to Annotate and leave a descriptive comment for your annotator: Export Dataset for Downstream Tasks Say you are building computer vision applications for an online retailer or want to develop an image search engine. You can export the dataset metadata for those tasks if you need to use the data downstream for preprocessing or training.  Back to your new collection, click the menu, and find the Generate CSV option to export the dataset metadata in your collection: Nice! Now, you have CSV with your dataset metadata and direct links to the images on Encored so that you can access them securely for downstream tasks. Next Steps: Labeling Your Products on Annotate After successfully using Encord Active to curate your image datasets through semantic search, the next critical step involves accurately labeling the data. Encord Annotate is a robust and intuitive platform for annotating images with high precision. This section explores how transitioning from dataset curation to dataset labeling within the Encord ecosystem can significantly improve the value and utility of the “White Sneakers” product listing. Encord Annotate supports various annotation types, including bounding boxes, polygons, segmentation masks, and keypoints for detailed representation of objects and features within images. Features of Encord Annotate Integrated Workflow: Encord Annotate integrates natively with Encord Active. This means a simple transition between searching, curating, and labeling. Collaborative Annotation: The platform supports collaborative annotation efforts that enable teams to work together efficiently, assign tasks, and manage project progress. Customizable Labeling Tools: You can customize the annotation features and labels to fit the specific needs of the projects to ensure that the data is labeled in the most relevant and informative way. Quality Assurance Mechanisms: Built-in quality assurance features, such as annotation review and validation processes, ensure the accuracy and consistency of the labeled data. AI-Assisted Labeling: Encord Annotate uses AI models like the Segment Anything Model (SAM) and LLaVA to suggest annotations, significantly speeding up the labeling process while maintaining high levels of precision. Getting Started with Encord Annotate To begin labeling the curated dataset with Encord Annotate, ensure it is properly organized and appears in Annotate.  New to Annotate? Sign up on this page to get started. From there, accessing Encord Annotate is straightforward: Navigate to the Encord Annotate section within the platform. Select the dataset you wish to annotate. Choose or customize the annotation tools and labels to your project requirements. Start the annotation process individually or by assigning tasks to team members. Key Takeaways: Semantic Search with Encord Active Phew! Glad you were able to join me and stick through the entire guide. Let’s recap what we have learned in this article: Encord Active uses CLIP for Image Retrieval: Encord Active runs CLIP under the hood to enable semantic search. This approach allows for more intuitive, accurate, and contextually relevant image retrieval than traditional keyword- or metadata-based searches. Natural Language Improves Image Exploration Experience: Searching by natural language within Encord Active makes searching for images as simple as describing what you're looking for in your own words. This feature significantly improves the search experience, making it accessible and efficient across various domains. Native Transition from Curation to Annotation: Encord Active and Encord Annotate provide an integrated workflow for curating and labeling datasets. Use this integration to curate data for upstream annotation tasks or downstream tasks (data cleaning or training computer vision models).