The Full Guide to Embeddings in Machine Learning

In modern AI-driven applications, Machine Learning Operations (MLOps) and Data Operations (DataOps) help manage machine learning and data-related operations. Their contribution through principles, practices, and tools is vital for scaling up ML and data applications.  Data Operations (DataOps) is an automated approach to streamlining and managing data at scale, so it is helpful for downstream tasks. MLOps and DataOps make collaborating easier for teams, automate tasks, manage large datasets, use sophisticated algorithms, and maintain models continuously. They also let teams focus on experimenting and coming up with new ideas. But what makes both processes effective for managing data and scaling ML projects?  It is important to note that DevOps practices have influenced both of these practices, and many approaches are borrowed or transferred from them. For instance, at their core, both rely on robust methodology and components that include version control, continuous integration/continuous deployment (CI/CD), monitoring and observability, and model governance.   Furthermore, both practices prioritize automation, collaboration, and streamlining various operations related to ML model development and data engineering and management.  This article explains the approaches involved in both practices. You will also learn the similarities and differences between both methodologies (MLOps and DataOps). MLOps Methodology MLOps largely depends on a methodology that optimizes the deployment and management of ML models in production environments. It merges machine learning (ML) with DevOps by adopting best practices from software development and operations to efficiently deploy and maintain models in production environments. You can view this methodology in three ways: Problem Definition and Data Acquisition: Identify the problem, gather the data, and design the solution. ML training and development: Train and implement proof of concept (PoC) models. Iteratively evaluate, retrain, and improve them to deliver a stable, high-performing model. Managing ML operations: Deploy, manage, and monitor models in production. This also involves automating experiments with various models, parameters, and new data. MLOps MLOps bridges model development and operations through CI/CD automation, workflow orchestration, collaboration, continuous ML training and evaluation, metadata tracking, monitoring, and feedback loops.  It helps avoid technical debt, ensures reproducibility, complies with governance, scales operations, fosters collaboration, and monitors performance.  ML Lifecycle in MLOps Another important aspect of MLOps is the ML lifecycle. An ML lifecycle is a set of procedures or methods that enables an ML practitioner to develop, deploy, and continuously maintain ML models in real-world settings.  It generally has four phases: Data Collection: This step ensures we collect and prepare data from different sources. Having a dataset from a legitimate source is vital. In addition, ensure that it is well-processed, curated, and ready for training models. Model Training: After that, data is well structured, engineered, and used to train ML models. Training is an iterative process to develop an optimal model. Deployment: Once trained, we deploy the model in real-world settings, where it can make predictions about new data in real-time. Monitoring: After deploying the model, we continuously monitor it to ensure it works well and maintains the expected performance. The monitoring process involves spotting bugs and inconsistencies in the model or changes in data patterns. Monitoring uses performance metrics to track the model's behavior and provide live feedback.  Keeping the above as the building blocks in the ML lifecycle, MLOps emphasizes continuous integration and deployment (CI/CD).  The CI/CD pipeline keeps the lifecycle streamlined and consistent. This allows ML practitioners to innovate and add new features much more quickly. CI/CD involves testing, validating, and deploying models automatically. Technically, it involves: Version Control: It keeps track of changes in code, data, and model parts. This allows you to trace the changes made over time. It also allows you to identify errors and bugs, leading to faster improvements.  Continuous Integration (CI): Automatically validates the codes to detect errors and ensure that the ML applications are production-ready.  Continuous Delivery (CD): Automates the deployment process of ML models to production environments, ensuring that models are deployed quickly and efficiently. Monitoring: This ensures the model and the complete end-to-end pipeline work well.  Now, it is essential to consider the role data plays. An ML model will only perform well if the data is consistent and accurate. For that, we need another set of practices that will allow us to engineer, curate, and analyze data appropriately and efficiently. This is where DataOps comes into the picture, and it is integrated with MLOps.  Integration of MLOps and DataOps DataOps is a process-oriented practice that ensures, maintains, and improves data quality. It is an essential tool when working with big data because it generally contains many inconsistencies and errors, along with vital information. What Is DataOps? DataOps includes various approaches, such as data engineering, quality, security, and integration. Along with these approaches, DataOps leverages principles and tools that allow data engineers and teams to curate and process consistent, well-balanced, and high-quality data for downstream tasks like analytics and ML development. The goal is to automate the data life cycle. It plays a crucial role in upholding the integrity, quality, and security of the data that acts as fuel for data-driven ML models. This ensures cleaner, high-quality data for model training.  MLOps, on the other hand, simplifies machine learning models for better management and logistics between operation teams and researchers. Integrating DataOps and MLOps leads to better-performing and more accurate model development. This enables organizations to improve the quality of production ML, increase automation, and focus on business requirements.   DataOps and MLOps share common steps, including data ingestion, preprocessing, model training, model deployment, and model monitoring. The data (pre-)processing part of MLOps focuses on moving data from the source to the ML model.  Recommended Read: Mastering Data Cleaning & Data Preprocessing.   The following section will discuss seven points highlighting similarities between MLOps and DataOps.  Similarities: MLOps and DataOps We discussed how MLOps and DataOps have much in common in the previous section. Now, let's dig into some of the shared features they both have: Automation: MLOps and DataOps automate processes to execute operations and reduce errors. Automation helps tidy up data pipelines, run ML models, and monitor them once deployed. Collaboration: These technologies emphasize the importance of teamwork. They are designed so that data scientists and engineers can collaborate to achieve a common goal. CI/CD: They both involve CI/CD practices, which means they like to get things out there quickly and update them easily. This is handy for rapidly spinning up data pipelines and training ML models. Model Cataloging and Version Control: MLOps and DataOps keep track of code, metadata, artifacts, etc. They catalog and keep data and ML model versions so everything stays consistent and can be reviewed later. Monitoring: DataOps monitors data pipelines, while MLOps monitors ML models. This helps catch bugs early on and ensures everything runs smoothly.  Governance: Both practices ensure that data is of good quality and safety and that everything follows the rules. This means complying with regulations like GDPR and HIPAA. DevOps Principles: Lastly, they draw inspiration from DevOps, which is all about teamwork, automation, and innovation.  The table below shows similarities between both practices in various aspects.  Differentiating MLOps and DataOps  Though these two fields share similar approaches, they each have their objective within the machine learning workflows.  Take DataOps, for instance. It's all about managing and delivering data. This involves improving data quality, streamlining data processes, etc. DataOps tools like Encord, Apache Airflow, Jenkins, Luigi, etc., orchestrate and automate data pipelines, perform data profiling, and check version control. Now, when it comes to MLOps, it is more about getting the ML models up and running efficiently in the real world. It also involves training, version control, monitoring, and fine-tuning performance. MLOps provides frameworks such as TensorFlow, PyTorch, or Keras to help with that. Automation tools like Neptune.ai, WandB, H2O.ai, DataRobot, etc. allow data scientists and ML engineers to monitor and track every component.  The table below compares the differences between both practices in various aspects. DataOps vs. MLOps: Which One Should You Choose? The choice between MLOps and DataOps largely depends on the specific focus and objectives of your project: Choose MLOps if: You are primarily concerned with developing, deploying, and managing production ML models, including overseeing the ML lifecycle.  Suppose you aim to perform one or all operations, such as efficiently deploying, monitoring, and maintaining ML models, focusing on aspects like version control, continuous integration/deployment, monitoring, and model governance. In that case, you should opt for MLOps. You want to streamline the ML lifecycle, automate experiments, ensure reproducibility, and scale operations efficiently. You want to scale the ML project. Because the ML lifecycle gets complicated as the size of the project scales up. The size affects how complicated your data processes are, how many models you handle, and how much automation and monitoring you need. For big projects, MLOps gives you a structured way to manage your ML models. This includes controlling versions, monitoring, and making sure they work well. Even with big and complex tasks, MLOps helps keep your models scalable, reproducible, and robust. Choose DataOps if: Your primary focus is collecting, managing, and delivering data within your organization. You aim to improve data quality, streamline processes, and optimize data delivery for downstream tasks (production models, business intelligence, analytics, etc.). You want to automate data pipelines, improve data quality, and ensure consistent, high-quality data for downstream tasks. You are working with big data or if you want to scale up the data streamlining process. Because DataOps focuses on managing data pipelines efficiently, making sure the data is good and easy to operate and access. When working with big data, you need to choose the right tools and ways to handle data input, change, and quality checks. With big data and larger projects, DataOps ensures your data pipelines can scale up, stay reliable, and keep data quality high throughout the process. DataOps Vs. MLOps: Key Takeaways In this article, we explored the various aspects of MLOps and dataOps. We studied the similarities and differences, the benefits of integrating both disciplines and which one to choose.  Integrating both can add value to the data and ML projects, as well as teams building data-intensive production ML applications.  See Also: Top 8 Use Cases of Computer Vision in Manufacturing.   Here is a summary of all that we covered in this article comparing DataOps and MLOps.  DataOps:  Focuses on improving data quality through methodologies like data engineering, quality assurance, and security measures. Aims to streamline data operations from end to end. MLOps, on the other hand: Bridges the gap between ML model development and deployment. Combines ML with DevOps, which includes designing ML-powered applications, experimentation and development, and ML operation.  Can be executed in four phases: Data Collection, Model Training, Deployment, and Monitoring.  We also covered the similarities of both MLOps and DataOps, which, in a nutshell:  Automate operations and create streamlined workflows. Focus on collaboration, workflow orchestration, monitoring, and version control. When it comes to the differences: MLOps offers tools for building, deploying, and monitoring ML models. DataOps offers tools for data engineering and managing datasets. Lastly, implementing both disciplines can be challenging due to the complexity of managing the machine learning lifecycle from experimentation to production and governing data quality and security.

What is overfitting in computer vision? Overfitting is a significant issue in computer vision where model learns the training data too well, including noise and irrelevant details. This leads to poor performance on new unseen data even if the model performs too well on training data. Overfitting occurs when the model memorizes specific patterns in the training images instead of learning general features. Overfit models have extremely high accuracy on the training data but much lower accuracy on testing data, failing to generalize well. Complex models with many parameters are more prone to overfitting, especially with limited training data.  In this blog, we will learn about What is the difference between overfitting and underfitting? How to find out if the model is overfitting? What to do if the model is overfitting? and how to use tools like Encord Active to detect and avoid overfitting. Overfitting Vs Underfitting: Key Differences Performance on training data: Overfitting leads to very high training accuracy while underfitting results in low training accuracy. Performance on test/validation data: Overfitting causes poor performance on unseen data, while underfitting also performs poorly on test/validation data. Model complexity: Overfitting is caused by excessive model complexity while underfitting is due to oversimplified models. Generalization: Overfitting models fail to generalize well, while underfit models cannot capture the necessary patterns for generalization. Bias-variance trade-off: Overfitting has high variance and low bias, while underfitting has high bias and low variance. Overfitting and Underfitting: Key Statistical Terminologies When training a machine learning model you are always trying to strike a balance between capturing the underlying patterns in the data while avoiding overfitting or underfitting. Here is a brief overview of the key statistical concepts which are important to understand for us to improve model performance and generalization.   Data Leakage Data leakage occurs when information from outside the training data is used to create the model. This can lead to a situation where the model performs exceptionally well on training data but poorly on unseen data. This can happen when data preprocessing steps, such as feature selection or data imputation, are performed using information from the entire dataset, including the test set. Bias Bias refers to the error introduced by approximating a real-world problem with a simplified model. A high bias model needs to be more accurate in order to capture the underlying patterns in the data, which leads to underfitting. Addressing bias involves increasing model complexity or using more informative features. For more information on how to address bias, read the blog How To Mitigate Bias in Machine Learning Models. Variance Variance is a measure of how much the model’s predictions fluctuate for different training datasets. A high variance model is overly complex and sensitive to small fluctuations in the training data and captures noise in the training dataset. This leads to overfitting and the machine learning model performs poorly on unseen data. Bias-variance tradeoff The bias-variance tradeoff illustrates the relationship between bias and variance in the model performance. Ideally, you would want to choose a model that both accurately captures the patterns in the training data, but also generalize well to unseen data.  Unfortunately, it is typically impossible to do both simultaneously. High-variance learning methods may be able to represent their training dataset well but are at risk of overfitting to noisy or unrepresented training data. In contrast, algorithms with a high bias typically produce simpler models that don’t tend to overfit but may underfit their training data, failing to capture the patterns in the dataset. Bias and variance are one of the fundamental concepts of machine learning. If you want to understand better with visualization, watch the video below. Bootstrap Bootstrapping is a statistical technique that involves resampling the original dataset with replacement to create multiple subsets or bootstrap samples. These bootstrap samples are then used to train multiple models, allowing for the estimation of model performance metrics, such as bias and variance, as well as confidence intervals for the model's predictions. K-Fold Cross-Validation K-Fold Cross-Validation is another resampling technique used to estimate a model's performance and generalization capability. The dataset is partitioned into K equal-sized subsets (folds). The model is trained on K-1 folds and evaluated on the remaining fold. This process is repeated K times, with each fold serving as the validation set once. The final performance metric is calculated as the average across all K iterations. LOOCV (Leave-One-Out Cross-Validation) Leave-One-Out Cross-Validation (LOOCV) is a special case of K-Fold Cross-Validation, where K is equal to the number of instances in the dataset. In LOOCV, the model is trained on all instances except one, and the remaining instance is used for validation. This process is repeated for each instance in the dataset, and the performance metric is calculated as the average across all iterations. LOOCV is computationally expensive but can provide a reliable estimate of model performance, especially for small datasets. Here is an amazing video by Josh Starmer explaining cross-validation. Watch it for more information. Assessing Model Fit Residual Analysis Residuals are the differences between the observed values and the values predicted by the model. Residual analysis involves examining the patterns and distributions of residuals to identify potential issues with the model fit. Ideally, residuals should be randomly distributed and exhibit no discernible patterns or trends. Structured patterns in the residuals may indicate that the model is missing important features or violating underlying assumptions. Goodness-of-Fit Tests Goodness-of-fit tests provide a quantitative measure of how well the model's predictions match the observed data. These tests typically involve calculating a test statistic and comparing it to a critical value or p-value to determine the significance of the deviation between the model and the data. Common goodness-of-fit tests include: Chi-squared test Kolmogorov-Smirnov test Anderson-Darling test The choice of test depends on the assumptions about the data distribution and the type of model being evaluated. Evaluation Metrics Evaluation metrics are quantitative measures that summarize the performance of a model on a specific task. Different metrics are appropriate for different types of problems, such as regression, classification, or ranking. Some commonly used evaluation metrics include: For regression problems: Mean Squared Error (MSE), Root Mean Squared Error (RMSE), R-squared (R²) For classification problems: Accuracy, Precision, Recall, F1-score, Area Under the Receiver Operating Characteristic Curve (AUROC) Diagnostic Plots Diagnostics plots, such as residual plots, quantile-quantile (Q-Q) plots, and calibration plots, can provide valuable insights into model fit. These graphical representations can help identify patterns, outliers, and deviations from the expected distributions, complementing the quantitative assessment of model fit. Causes for Overfitting in Computer Vision Here are the following causes for overfitting in computer vision: High Model Complexity Relative to Data Size One of the primary causes of overfitting is when the model's complexity is disproportionately high compared to the size of the training dataset. Deep neural networks, especially those used in computer vision tasks, often have millions or billions of parameters. If the training data is limited, the model can easily memorize the training examples, including their noise and peculiarities, rather than learning the underlying patterns that generalize well to new data. Noise Training Data Image or video datasets, particularly those curated from real-world scenarios, can contain a significant amount of noise, such as variations in lighting, occlusions, or irrelevant background clutter. If the training data is noisy, the model may learn to fit this noise instead of focusing on the relevant features. Insufficient Regularization Regularization techniques, such as L1 and L2 regularization, dropout, or early stopping, are essential for preventing overfitting in deep learning models. These techniques introduce constraints or penalties that discourage the model from learning overly complex patterns that are specific to the training data. With proper regularization, models can easily fit, especially when dealing with high-dimensional image data and deep network architectures. Data Leakage Between Training/Validation Sets Data leakage occurs when information from the test or validation set is inadvertently used during the training process. This can happen due to improper data partitioning, preprocessing steps that involve the entire dataset, or other unintentional sources of information sharing between the training and evaluation data. Even minor data leakage can lead to overly optimistic performance estimates and a failure to generalize to truly unseen data. How to Detect an Overfit Model? Here are some common techniques to detect an overfit model: Monitoring the Training and Validation/Test Error During the training process, track the model’s performance on both the training and validation/test datasets. If the training error continues to decrease while the validation/test error starts to increase or plateau, a strong indication of overfitting. An overfit model will have a significantly lower training error compared to the validation/test error. Learning Curves Plot learning curves that show the training and validation/test error as a function of the training set size. If the training error continues to decrease while the validation/test error remains high or starts to increase as more data is added, it suggests overfitting. An overfit model will have a large gap between the training and validation/test error curves. Cross-Validation Perform k-fold cross-validation on the training data to get an estimate of the model's performance on unseen data. If the cross-validation error is significantly higher than the training error, it may indicate overfitting. Regularization Apply regularization techniques, such as L1 (Lasso) or L2 (Ridge) regularization, dropout, or early stopping. If adding regularization significantly improves the model's performance on the validation/test set while slightly increasing the training error, it suggests that the original model was overfitting. Model Complexity Analysis Examine the model's complexity, such as the number of parameters or the depth of a neural network. A highly complex model with a large number of parameters or layers may be more prone to overfitting, especially when the training data is limited. Visualization For certain types of models, like decision trees or neural networks, visualizing the learned representations or decision boundaries can provide insights into overfitting. If the model has overly complex decision boundaries or representations that appear to fit the training data too closely, it may be an indication of overfitting. Ways to Avoid Overfitting in Computer Vision Data Augmentation Data augmentation techniques, such as rotation, flipping, scaling, and translation, can be applied to the training dataset to increase its diversity and variability. This helps the model learn more robust features and prevents it from overfitting to specific data points. Observe and Monitor the Class Distributions of Annotated Samples During annotation, observe class distributions in the dataset. If certain classes are underrepresented, use active learning to prioritize labeling unlabeled samples from those minority classes. Encord Active can help find similar images or objects to the underrepresented classes, allowing you to prioritize labeling them, thereby reducing data bias. Finding similar images in Encord Active. Early Stopping Early stopping is a regularization technique that involves monitoring the model's performance on a validation set during training. If the validation loss stops decreasing or starts to increase, it may indicate that the model is overfitting to the training data. In such cases, the training process can be stopped early to prevent further overfitting. Dropout Dropout is another regularization technique that randomly drops (sets to zero) a fraction of the activations in a neural network during training. This helps prevent the model from relying too heavily on any specific set of features and encourages it to learn more robust and distributed representations. L1 and L2 Regularization L1 and L2 regularization techniques add a penalty term to the loss function, which discourages the model from having large weights. This helps prevent overfitting by encouraging the model to learn simpler and more generalizable representations. Transfer Learning Transfer learning involves using a pre-trained model on a large dataset (e.g., ImageNet) as a starting point for training on a new, smaller dataset. The pre-trained model has already learned useful features, which can help prevent overfitting and improve generalization on the new task. Ensemble Methods Ensemble methods, such as bagging (e.g., random forests) and boosting (e.g., AdaBoost), combine multiple models to make predictions. These techniques can help reduce overfitting by averaging out the individual biases and errors of the component models. For more information, read the blog What is Ensemble Learning?   Model Evaluation  Regularly monitoring the model's performance on a held-out test set and evaluating its generalization capabilities is essential for detecting and addressing overfitting issues. Using Encord Active to Reduce Model Overfitting Encord Active is a comprehensive platform offering features to curate a dataset that can help reduce the model overfitting and evaluate the model’s performance to identify and address any potential issues. Here are a few of the ways Encord Active can be used to reduce model overfitting: Evaluating Training Data with Data and Label Quality Metrics Encord Active allows users to assess the quality of their training data with data quality metrics. It provides metrics such as missing values, data distribution, and outliers. By identifying and addressing data anomalies, practitioners can ensure that their dataset is robust and representative. Encord Active also allows you to ensure accurate and consistent labels for your training dataset. The label quality metrics, along with the label consistency checks and label distribution analysis help in finding noise or anomalies which contribute to overfitting. Evaluating Model Performance with Model Quality Metrics After training a model, it’s essential to evaluate its performance thoroughly. Encord Active provides a range of model quality metrics, including accuracy, precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC-ROC). These metrics help practitioners understand how well their model generalizes to unseen data and identify the data points which contribute to overfitting. Active Learning Workflow Overfitting often occurs when models are trained on insufficient or noisy data. Encord Active incorporates active learning techniques, allowing users to iteratively select the most informative samples for labeling. By actively choosing which data points to label, practitioners can improve model performance while minimizing overfitting.

Multimodal deep learning is a recent trend in artificial intelligence (AI) that is revolutionizing how machines understand the real world using multiple data modalities, such as images, text, video, and audio.  In particular, multiple machine learning frameworks are emerging that exploit visual representations to infer textual descriptions following Open AI’s introduction of the Contrastive Language-Image Pre-Training (CLIP) model. The improved models use more complex datasets to change the CLIP framework for use cases that are specific to the domain. They also have better state-of-the-art (SoTA) generalization performance than the models that came before them. This article discusses the benefits, challenges, and alternatives of Open AI CLIP to help you choose a model for your specific domain. The list below mentions the architectures covered: Pubmed CLIP PLIP SigLIP Street CLIP Fashion CLIP  CLIP-Rscid BioCLIP CLIPBert Open AI CLIP Model CLIP is an open-source vision-language AI model by OpenAI trained using image and natural language data to perform zero-shot classification tasks. Users can provide textual captions and use the model to assign a relevant label to the query image. Open AI CLIP Model: Architecture and Development The training data consists of images from the internet and 32,768 text snippets assigned to each image as its label. The training task involves using natural language processing (NLP) to predict which label goes with which image by understanding visual concepts and relating them to the textual data. CLIP Architecture The model primarily uses an image and a text encoder that convert images and labels into embeddings. Optimization involves minimizing a contrastive loss function by computing similarity scores between these embeddings and associating the correct label with an image. See Also: What is Vector Similarity Search?   Once trained, the user can provide an unseen image as input with multiple captions to the image and text encoders. CLIP will then predict the correct label that goes with the image. Benefits of OpenAI CLIP OpenAI CLIP has multiple benefits over traditional vision models. The list below mentions the most prominent advantages: Zero-shot Learning (ZSL): CLIP’s training approach allows it to label unseen images without requiring expensive training on new datasets. Like Generative Pre-trained Transformer - 3 (GPT-3) and GPT-4, CLIP can perform zero-shot classification tasks using natural language data with minimal training overhead. The property also helps users fine-tune CLIP more quickly to adapt to new tasks. Better Real-World Performance: CLIP demonstrates better real-world performance than traditional vision models, which only work well with benchmark datasets. Limitations of OpenAI CLIP Although CLIP is a robust framework, it has a few limitations, as highlighted below: Poor Performance on Fine-grained Tasks: CLIP needs to improve its classification performance for fine-grained tasks such as distinguishing between car models, animal species, flower types, etc. Out-of-Distribution Data: While CLIP performs well on data with distributions similar to its training set, performance drops when it encounters out-of-distribution data. The model requires more diverse image pre-training to generalize to entirely novel tasks. Inherent Social Bias: The training data used for CLIP consists of randomly curated images with labels from the internet. The approach implies the model learns intrinsic biases present in image captions as the image-text pairs do not undergo filtration. Due to these limitations, the following section will discuss a few alternatives for domain-specific tasks. Learn how to build visual search engines with CLIP and ChatGPT in our on-demand webinar.   Alternatives to CLIP Since CLIP’s introduction, multiple vision-language algorithms have emerged with unique capabilities for solving problems in healthcare, fashion, retail, etc. We will discuss a few alternative models that use the CLIP framework as their base. We will also briefly mention their architecture, development approaches, performance results, and use cases. 1. PubmedCLIP PubmedCLIP is a fine-tuned version of CLIP for medical visual question-answering (MedVQA), which involves answering natural language questions about an image containing medical information. PubmedCLIP: Architecture and Development The model is pre-trained on the Radiology Objects in Context (ROCO) dataset, which consists of 80,000 samples with multiple image modalities, such as X-ray, fluoroscopy, mammography, etc. The image-text pairs come from Pubmed articles; each text snippet briefly describes the image’s content. PubmedCLIP Architecture Pre-training includes fine-tuning CLIP’s image and text encoders to minimize contrastive language and vision loss. The pretrained module, PubMedCLIP, and a Convolutional Denoising Image Autoencoder (CDAE) encode images. A question encoder converts natural language questions into embeddings and combines them with the encoded image through a bilinear attention network (BAN). The training objective is to map the embeddings with the correct answer by minimizing answer classification and image reconstruction loss using a CDAE decoder. Performance Results of PubmedCLIP The accuracy metric shows an improvement of 1% compared to CLIP on the VQA-RAD dataset, while PubMedCLIP with the vision transform ViT-32 as the backend shows an improvement of 3% on the SLAKE dataset. See Also: Introduction to Vision Transformers (ViT).   PubmedCLIP: Use Case Healthcare professionals can use PubMedCLIP to interpret complex medical images for better diagnosis and patient care. 2. PLIP The Pathology Language-Image Pre-Training (PLIP) model is a CLIP-based framework trained on extensive, high-quality pathological data curated from open social media platforms such as medical Twitter. PLIP: Architecture and Development Researchers used 32 pathology hashtags according to the recommendations of the United States Canadian Academy for Pathology (USCAP) and the Pathology Hashtag Ontology project. The hashtags helped them retrieve relevant tweets containing de-identified pathology images and natural descriptions. The final dataset - OpenPath - comprises 116,504 image-text pairs from Twitter posts, 59,869 image-text pairs from the corresponding replies with the highest likes, and 32,041 additional image-text pairs from the internet and the LAION dataset. OpenPath Dataset Experts use OpenPath to fine-tune CLIP through an image preprocessing pipeline that involves image down-sampling, augmentations, and random cropping. Performance Results of PLIP PLIP achieved state-of-the-art (SoTA) performance across four benchmark datasets. On average, PLIP achieved an F1 score of 0.891, while CLIP scored 0.813. PLIP: Use Case PLIP aims to classify pathological images for multiple medical diagnostic tasks and help retrieve unique pathological cases through image or natural language search. New to medical imaging? Check out ‘Guide to Experiments for Medical Imaging in Machine Learning.’   3. SigLip SigLip uses a more straightforward sigmoid loss function to optimize the training process instead of a softmax contrastive loss as traditionally used in CLIP. The method boosts training efficiency and allows users to scale the process when developing models using more extensive datasets. SigLip: Architecture and Development Optimizing the contrastive loss function implies maximizing the distance between non-matching image-text pairs while minimizing the distance between matching pairs. However, the method requires text-to-image and image-to-text permutations across all images and text captions. It also involves computing normalization factors to calculate a softmax loss. The approach is computationally expensive and memory-inefficient. Instead, the sigmoid loss simplifies the technique by converting the loss into a binary classification problem by assigning a positive label to matching pairs and negative labels to non-matching combinations. Efficient Loss Implementation In addition, permutations occur on multiple devices, with each device predicting positive and negative labels for each image-text pair. Later, the devices swap the text snippets to re-compute the loss with corresponding images. Performance Results of SigLip Based on the accuracy metric, the sigmoid loss outperforms the softmax loss for smaller batch sizes on the ImageNet dataset. Performance comparison Both losses deteriorate after a specific batch size, with Softmax performing slightly better at substantial batch sizes. SigLip: Use Case SigLip is suitable for training tasks involving extensive datasets. Users can fine-tune SigLip using smaller batch sizes for faster training. 4. StreetCLIP StreetCLIP is an image geolocalization algorithm that fine-tunes CLIP on geolocation data to predict the locations of particular images. The model is available on Hugging Face for further research. StreetCLIP: Architecture and Development The model improves CLIP zero-shot learning capabilities by training a generalized zero-shot learning (GZSL) classifier that classifies seen and unseen images simultaneously during the training process. StreetCLIP Architecture Fine-tuning involves generating synthetic captions for each image, specifying the city, country, and region. The training objective is to correctly predict these three labels for seen and unseen photos by optimizing a GZSL and a vision representation loss. Performance Results of StreetCLIP Compared to CLIP, StreetCLIP has better geolocation prediction accuracy. It outperforms CLIP by 0.3 to 2.4 percentage points on the IM2GPS and IM2GPS3K benchmarks. StreetCLIP: Use Case StreetCLIP is suitable for navigational purposes where users require information on weather, seasons, climate patterns, etc. It will also help intelligence agencies and journalists extract geographical information from crime scenes. 5. FashionCLIP FashionCLIP (F-CLIP) fine-tunes the CLIP model using fashion datasets consisting of apparel images and textual descriptions. The model is available on GitHub and HuggingFace. FashionCLIP: Architecture and Development The researchers trained the model on 700k image-text pairs in the Farfetch inventory dataset and evaluated it on image retrieval and classification tasks. F-CLIP Architecture The evaluation also involved testing for grounding capability. For instance, zero-shot segmentation assessed whether the model understood fashion concepts such as sleeve length, brands, textures, and colors. They also evaluated compositional understanding by creating improbable objects to see if F-CLIP generated appropriate captions. For instance, they see if F-CLIP can generate a caption—a Nike dress—when seeing a picture of a long dress with the Nike symbol. Performance Results of FashionCLIP F-CLIP outperforms CLIP on multiple benchmark datasets for multi-modal retrieval and product classification tasks. For instance, F-CLIP's F1 score for product classification is 0.71 on the F-MNIST dataset, while it is 0.66 for CLIP. FashionCLIP: Use Case Retailers can use F-CLIP to build chatbots for their e-commerce sites to help customers find relevant products based on specific text prompts. The model can also help users build image-generation applications for visualizing new product designs based on textual descriptions. 6. CLIP-RSICD CLIP-RSICD is a fine-tuned version of CLIP trained on the Remote Sensing Image Caption Dataset (RSICD). It is based on Flax, a neural network library for JAX (a Python package for high-end computing). Users can implement the model on a CPU. The model is available on GitHub. CLIP-RSICD: Architecture and Development The RSICD consists of 10,000 images from Google Earth, Baidu Map, MapABC, and Tianditu. Each image has multiple resolutions with five captions. RSICD Dataset Due to the small dataset, the developers implemented augmentation techniques using transforms in Pytorch’s Torchvision package. Transformations included random cropping, random resizing and cropping, color jitter, and random horizontal and vertical flipping. Performance Results of CLIP-RSICD On the RSICD test set, the regular CLIP model had an accuracy of 0.572, while CLIP-RSICD had a 0.883 accuracy score. CLIP-RSICD: Use Case CLIP-RSICD is best for extracting information from satellite images and drone footage. It can also help identify red flags in specific regions to predict natural disasters due to climate change. 7. BioCLIP BioCLIP is a foundation model for the tree of life trained on an extensive biology image dataset to classify biological organisms according to their taxonomy. BioCLIP: Architecture and Development BioCLIP fine-tunes the CLIP framework on a custom-curated dataset—TreeOfLife-10M—comprising 10 million images with 454 thousand taxa in the tree of life. Each taxon corresponds to a single image and describes its kingdom, phylum, class, order, family, genus, and species. Taxonomic Labels The CLIP model takes the taxonomy as a flattened string and matches the description with the correct image by optimizing the contrastive loss function. Researchers also enhance the training process by providing scientific and common names for a particular species to improve generalization performance. This method helps the model recognize a species through a general name used in a common language. Performance Results of BioCLIP On average, BioCLIP boosts accuracy by 18% on zero-shot classification tasks compared to CLIP on ten different biological datasets. BioCLIP: Use Case BioCLIP is ideal for biological research involving VQA tasks where experts quickly want information about specific species. Watch Also: How to Fine Tune Foundation Models to Auto-Label Training Data.   8. CLIPBert CLIPBert is a video and language model that uses the sparse sampling strategy to classify video clips belonging to diverse domains quickly. It uses Bi-directional Encoder Representations from Transformers (BERT) - a large language model (LLM), as its text encoder and ResNet-50 as the visual encoder. CLIPBert: Architecture and Development The model’s sparse sampling method uses only a few sampled clips from a video in each training step to extract visual features through a convolutional neural network (CNN). The strategy improves training speed compared to methods that use full video streams to extract dense features. The model initializes the BERT with weights pre-trained on BookCorpus and English Wikipedia to get word embeddings from textual descriptions of corresponding video clips. CLIPBert Training involves correctly predicting a video’s description by combining each clip’s predictions and comparing them with the ground truth. The researchers used 8 NVIDIA V100 GPUs to train the model on 40 epochs for four days. During inference, the model samples multiple clips and aggregates the prediction for each clip to give a final video-level prediction. Performance Results of CLIPBert CLIPBert outperforms multiple SoTA models on video retrieval and question-answering tasks. For instance, CLIPBert shows a 4% improvement over HERO on video retrieval tasks. CLIPBert: Use Case CLIPBert can help users analyze complex videos and allow them to develop generative AI tools for video content creation. See Also:  FastViT: Hybrid Vision Transformer with Structural Reparameterization. . Alternatives to Open AI CLIP: Key Takeaways With frameworks like CLIP and ChatGPT, combining computer vision with NLP is becoming the new norm for developing advanced multi-modal models to solve modern industrial problems. Below are a few critical points to remember regarding CLIP and its alternatives. OpenAI CLIP Benefits: OpenAI CLIP is an excellent choice for general vision-language tasks requiring low domain-specific expertise. Limitations: While CLIP’s zero-shot capability helps users adapt the model to new tasks, it underperforms on fine-grained tasks and out-of-distribution data. Alternatives: Multiple CLIP-based options exist that are suitable for medical image analysis, biological research, geo-localization, fashion, and video understanding.