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 domain-specific use cases. 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 🔥 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. 🔥 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 are suitable for medical image analysis, biological research, geo-localization, fashion, and video understanding. 🔥 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. 🔥

The AI landscape has been revolutionized with the advent of tools and platforms that offer enhanced functionality and real-time capabilities. Founded by Alexandr Wang, Scale AI has emerged as a key player, offering a template for high-quality data infrastructure.  As with any industry leader, however, new entrants offer a fresh perspective and more cost-effective solutions.  As we delve into the best alternatives to Scale AI, we'll explore platforms that offer an enhanced user experience, specializing in data labeling, cater to large-scale operations, and can handle many users. From platforms that leverage neural networks to those that focus on transcription, the future of AI is diverse and promising. Encord  Encord offers a suite of tools designed to accelerate the creation of training data. Encord's annotation platform is powered by AI-assisted labeling, enabling users to develop high-quality training data and deploy models up to 10 times faster. Encord’s active learning toolkit allows you to evaluate your models, and curate the most valuable data for labeling. ML Pipeline & Features State-of-the-art AI-assisted labeling and workflow tooling platform powered by micro-models Perfect for image, video, DICOM, and SAR annotation, labeling, QA workflows, and training computer vision models Native support for a wide range of annotation types, including bounding box, polygon, polyline, instance segmentation, keypoints, classification, and more Easy collaboration, annotator management, and QA workflows to track annotator performance and ensure high-quality labels Utilizes quality metrics to evaluate and improve ML pipeline performance across data collection, labeling, and model training stages Effortlessly search and curate data using natural language search across images, videos, DICOM files, labels, and metadata Auto-detect and resolve dataset biases, errors, and anomalies like outliers, duplication, and labeling mistakes Export, re-label, augment, review, or delete outliers from your dataset Robust security functionality with label audit trails, encryption, and compliance with FDA, CE, and HIPAA regulations Expert data labeling services on-demand for all industries Advanced Python SDK and API access for seamless integration and easy export into JSON and COCO formats Integration and Compatibility Encord offers robust integration capabilities, allowing users to import data from their preferred storage buckets and build pipelines for annotation, validation, model training, and auditing. The platform also supports programmatic automation, ensuring seamless workflows and efficient data operations. Benefits and Customer Feedback Its users have received Encord positively, with many highlighting the platform's efficiency in reducing false acceptance rates and its ability to train models on high-qualitydatasets. The platform's emphasis on AI-assisted labeling and active learning has been particularly appreciated, ensuring accurate and rapid training data creation. Learn more about how computer vision teams use Encord Vida reduce their model false positive from 6% to 1% Floy reduce CT & MRI annotation times by ~50% Stanford Medicine reduce experiment time by 80% King's College London increase labeling efficiency by 6.4x Tractable go through hyper-growth supported by faster annotation operations  iMerit iMerit specializes in providing data annotation solutions, including those for LiDAR, which is crucial for applications like autonomous vehicles and robotics. With a focus on complex data types, iMerit ensures high precision and quality in its annotations, making it a preferred choice for industries that require intricate data labeling. ML Pipeline and Features Expertise in LiDAR data annotation, ensuring accurate and high-quality annotations While Scale AI is known for its broad range of data labeling services, iMerit's strength lies in its specialization in complex data types, most notably LiDAR Robust integration options, allowing seamless connection with various platforms and tools Various tools and platforms for efficient data annotation and management Emphasis on compliance and data protection, ensuring that businesses can trust them with their sensitive data Benefits and Customer Feedback iMerit has garnered positive feedback from its clientele, particularly for its expertise in LiDAR data annotation. Many users have highlighted the platform's precision, efficiency, and quality of annotations. The platform's ability to handle complex data types and provide tailored solutions has been particularly appreciated, making it a go-to solution for industries like autonomous driving and robotics. Refer to the G2 Link for customer feedback on the iMerit platform. Dataloop Dataloop, an AI-driven data management platform, is tailored to streamline the process of generating data for AI. While Scale AI is recognized for its human-centric approach to data labeling, Dataloop differentiates itself with its cloud-based platform, providing flexibility and scalability for organizations of all sizes. ML Pipeline & Features Streamlines administrative tasks efficiently, organizing management and numerical data. Dataloop's object tracking and detection feature stands out, providing users with exceptional data quality Requires a stable and fast internet connection, which might pose challenges in areas with connectivity issues. Integration and Compatibility Dataloop, being a cloud-based platform, offers the advantage of flexibility. However, it also requires a stable and fast internet connection, which might pose challenges in areas with connectivity issues. Despite this, its integration capabilities ensure users can seamlessly connect their data sources and ML models to the platform. Benefits and Customer Feedback Dataloop has received positive feedback from its users. Users have noted the platform's scalability and flexibility, making it suitable for both small projects and larger needs. However, some users have pointed out that the user interface can be challenging to navigate, suggesting the need for tutorials or a more intuitive design. Here is the G2 link for customer reviews on the Dataloop platform. SuperAnnotate SuperAnnotate offers tools to streamline annotation. Their platform is equipped with tools and automation features that enable the creation of accurate training data across multiple data types. SuperAnnotate's offerings include the LLM Editor, Image Editor, Video Editor, Text Editor, LiDAR Editor, and Audio Editor. ML Pipeline &  Features Features like data insights, versioning, and a query system to filter and find relevant data Marketplace of over 400 annotation teams that speak 18 languages. This ensures high-quality annotations tailored to specific regional and linguistic requirements  Dedicated annotation project managers, ensuring stellar project delivery Annotation tools for different data types, from images and videos to LiDAR and audio Certifications like SOC 2 Type 2, ISO 27001, and HIPAA Data integrations with major cloud platforms like AWS, Azure, and GCP Benefits and Customer Feedback SuperAnnotate has received positive user feedback, with companies like Hinge Health praising the platform's high and consistent quality. Refer to the G2 link for customers' thoughts about the SuperAnnotate platform. Labelbox Labelbox, a leading data labeling platform, is designed to focus on collaboration and automation. It offers a centralized hub where teams can create, manage, and maintain high-quality training data. Labelbox provides tools for image, video, and text annotations. ML Pipeline & Features Labelbox supports data collection to model training Features include MAL (Model Assisted Labeling), which uses pre-trained models to accelerate the labeling process Easy collaboration, allowing multiple team members to work on the same dataset and ensuring annotation consistencyReviewer Workflow feature enables quality assurance by allowing senior team members to review and approve annotations Ontology Manager provides a centralized location to manage labeling instructions, ensuring clarity and consistency API integrations, allowing users to connect their data sources and ML models to the platform Supports integrations with popular cloud storage solutions Integration and Compatibility Labelbox offers API integrations, allowing users to connect their data sources and ML models seamlessly to the platform. This ensures a workflow from data ingestion to model training. The platform also supports integrations with popular cloud storage solutions, ensuring flexibility in data management. Here is the G2 link for customer reviews about the LabelBox platform. Scale Alternatives: Key Takeaways Scale’s interactive platform has been recognized for its excellent automation and streamlined workflows tailored for various use cases. While many platforms in the market are open-source, Scale AI's proposition lies in its focus on machine learning and AI-powered algorithms. The platform offers a range of plugins and tools that provide metrics and insights in real-time. With its robust API integrations, it seamlessly connects with platforms like Amazon, ensuring that artificial intelligence is leveraged to its full potential. The user-friendly interface of Scale AI, combined with its suite of AI tools, facilitates creating and managing datasets. This has made it a preferred choice for industries ranging from social media giants to tech behemoths like Microsoft. Scale AI's platform ensures seamless integration and functionality using Windows, iOS, or any other operating system. The semantic understanding and capabilities of platforms like GPT-3 have further underscored the importance of training data in sectors like healthcare. With companies like OpenAI launching tools like ChatGPT, the emphasis on NLP (Natural Language Processing) and computer vision has never been higher.  Platforms that are self-hosted, offer podcast transcription services, or focus on pixel-perfect data labeling are gaining traction. The rise of chatbots and tools that optimize customer support using GPT-4 and other advanced algorithms is reshaping the landscape. In this rapidly evolving domain, optimizing workflows and harnessing the power of natural language processing is paramount.  Here are our key takeaways: The AI domain is witnessing a transformative phase with new platforms and tools emerging. As industries seek efficient data labeling and management solutions, platforms like Encord are becoming indispensable. Encord's AI-assisted labeling accelerates the creation of high-quality training data, making it a prime choice in this evolving landscape. One of the standout features of modern AI platforms is the ability to harness AI for faster and more accurate data annotation. Encord excels in this with its AI-powered labeling, enabling users to annotate visual data swiftly and deploy models up to 10 times faster than traditional methods.