Building Production-Grade spaCy Text Classification Pipelines for Business Data
Unlock the full potential of spaCy with this guide to building production-grade text classification pipelines for business data.
Dlib is an open source suite of applications and libraries written in C++ under a permissive Boost license. Dlib offers a wide range of functionality across a number of machine learning sectors, including classification and regression, numerical algorithms such as quadratic program solvers, an array of image processing tools, and diverse networking functionality, among many other facets.
Dlib also features robust tools for object pose estimation, object tracking, face detection (classifying a perceived object as a face) and face recognition (identifying a perceived face).
Though Dlib is a cross-platform resource, many custom workflows involving facial capture and analysis (whether recognition or detection) use the OpenCV library of functions, operating in a Python environment, as in the image below.
There are a number of novel APIs and interfaces for Dlib, many of which provide additional functionality not directly anticipated by the original creators, such as real-time recognition of multiple faces.
Programmer Adam Geitgey offers a FOSS face recognition API that leverages Dlib.
Geitgey's CUDA-capable library has demonstrated 99.83% accuracy on the University of Massachusetts' Labeled Faces in the Wild benchmark dataset. The project encodes Dlib's face captures into 128 data points per face, resulting in unique parameters for the hash of each face across a variety of different photos. Subsequently a Support Vector Machine (SVM) is trained on the derived faces via scikit-learn, resulting in an agile FR model that can run with minimal latency in the right conditions.
One popular Chinese GitHub repository uses Dlib to power facial reconstruction, and the eos and 4dface libraries to compute geometry and capture textures, for the creation of photorealistic mesh heads.
Many projects that utilize Dlib are themselves intended as tool-chain resources, such as Drishti, a real-time eye-tracking framework written in C++11, and intended for iOS and Android devices, as well as embedded ARM and other lightweight computing environments.
Drishti can make use of CMake for iOS deployments, with native Xcode generation smoothing the development process on Apple's platform. Since CMake retains a few extra issues in Android Studio, the developers offer some workarounds to implement Drishti in Android.
Get Me Through is a Python-based FOSS solution for recognizing and admitting invitees to an event. Besides Dlib, the project uses MongoDB and Node.js v8.1.4+, is written in C++ 11, and supports MacOS and Linux, with untested support for Windows.
A number of repositories use Dlib as the facial recognition engine for attendance monitoring frameworks. One such project from India offers an automated pipeline, including a webcam recognition framework and the automation of warning mails to students that were not registered by the system during an attendance period.
Another India-based C++ GitHub project powered by Dlib uses facial captures to generate stylized anime avatars
Dlib is increasingly being used in image synthesis applications that involve the reconstruction of faces, style transfer, or deepfake images. One legitimate use for the latter is the anonymizing of faces of 'at risk' subjects, as was accomplished for the 2020 release Welcome To Chechnya, where a modified version of DeepFaceLab was employed to superimpose 'alternative' faces on interview subjects.
DeepFaceLab offers Dlib as a face extraction tool, together with the Python library MTCNN, which has its own strengths, but is prone to return more false positives than Dlib. Other popular face recognition libraries include Single Shot Scale-invariant Face Detector (S3FD), which can operate well on a mobile GPU, but may not get access to it, depending on resource allocation; and which runs poorly on CPU, compared to its stablemates.
To aid early development of a facial recognition/detection framework in your particular operating environment, you can compare Dlib's performance to its peers with Awesome face detection, which allows you to pit six competing libraries against each other: OpenCV Haar cascade, Dlib HoG (see below), Dlib CNN (see below), MTCNN, S3FD and InsightFace.
There are many more directly commercial use cases for Dlib's face detection capabilities, where the objective is to individuate a face from images or a video stream. These include:
Naturally, face detection also operates as a precursor or initial phase for facial recognition, where the system will attempt to maintain consistent focus on an identified face and run its characteristics through a database that's likely to turn up an ID match.
In private corporate environments, facial recognition can be used to monitor attendance and facilitate security access in diverse ways, from gaining building access to unlocking timed-out workstations, among a myriad of other possibilities. Other uses include:
Dlib is incredibly fast and very lightweight. It can comfortably operate at 30fps in standard environments, and can potentially detect facial landmarks in a single millisecond, though only in the most ideal conditions. It can also operate on hardware as basic as a Raspberry PI.
Additionally, it's possible to train Dlib to identify specific shape traits in a face, for general research or for medical applications.
Dlib offers two different functions for facial capture:
HoG + Linear SVM
The Histogram of Oriented Gradients (HoG) + Linear Support Vector Machine (SVM) algorithm in Dlib offers very fast recognition of front-on faces, but has limited capabilities in terms of recognizing face poses at acute angles (such as CCTV footage, or casual surveillance environments where the subject is not actively participating in the ID process).
It also supports passport-style profile faces, though with very little margin for error (faces pointing up or down, etc.). HoG + SVM is suitable for constrained situations where the sensor can expect a direct and unobstructed view of the participant's face, such as ATM and mobile framework ID systems, as well as mobile traffic surveillance recognition systems, where cameras are able to obtain a straight profile shot of drivers.
Max-Margin (MMOD) CNN face detector
MMOD is a robust and reliable, GPU-accelerated face detector that leverages a convolutional neural network (CNN), and is far more capable of capturing faces at obscure angles and in challenging conditions, suiting it for casual surveillance and urban analysis.
MMOD is not a distinct alternative to HoG + Linear SVM, but rather can be applied to HoG itself, or to any bag-of-visual-word model, which treats discovered pixel groupings as explorable entities for potential labeling — including the identification of faces.
In such cases, these explorable entities are discovered via a three-step process:
1: Feature Extraction
Where key points in the image are detected and assigned to Scale-Invariant Feature Transform (SIFT) features.
2: Codebook/'Vocabulary' Construction (normally k-means)
At this point, it's necessary to classify the discovered groups, and to segment them from background information. Unsupervised K-Means clustering can accomplish this well by iterating over all this unlabeled data until it has calculated the minimal sum of squared distances between all the captured points and the center of the cluster. When all those centers have been calculated, each will form the apex of a grouping, which can be fed into the next stage.
3: Vector Quantization
Vector Quantization (VQ) hails from early signal processing research, and has been a central plank of compression technologies, since it deals with the definition of minimum units from a 'noisy' environment. In our work-flow, VQ calculates the number of clusters found in stage #2 (see above) against the frequency of recurring patterns in order to provide a feature representation layer, and converge the estimated groupings into usefully distinct entities.
The appeal of HoG + Linear SVM under Dlib is its low use of resources; its efficacy when operating on CPU; the fact that it has at least some latitude for non-frontal faces; its low-impact model requirements; and a relatively capable occlusion detection routine.
Negatively, a default deployment requires a minimum face-size of 80x80 pixels. If you need to detect faces below this threshold, you'll need to train your own implementation. Additionally, this approach gives poor results on acute face angles; generates bounding boxes that may over-crop facial features; and struggles with challenging occlusion cases.
The advantage of MMOD (CNN) under Dlib is (perhaps above all) its ability to recognize difficult face orientations (which may be the deciding factor, depending on your target environment); its impressive speed when allowed access to even a moderately-specced GPU; its lightweight training architecture; and its superior occlusion handling.
Negatively, it can produce bounding boxes even more restricted than HoG + Linear SVM in a default deployment; performs notably more slowly on a CPU than HoG/LSVM; and shares HoG/LSVM's native inability to detect faces smaller than 80 pixels square — again, necessitating a custom build for certain scenarios, such as acute street surveillance viewpoints that extend into the distance.
The creator of MMOD, Davis King, has provided a number of useful open source trained models for Dlib, many (but not all) of which center on facial recognition. These include:
A Dlib Face Recognition Network model with 29 convolutional layers, an optimized version of the well-used ResNet-34 network. This model was trained on three million faces across various datasets, including Face Scrub, Oxford's VGG set, and the author's own web-scraped data.
An Imperial College London dataset designed for HoG, which is excluded from commercial use.
Dlib is a versatile and well-diffused facial recognition library, with perhaps an ideal balance of resource usage, accuracy and latency, suited for real-time face recognition in mobile app development. It's becoming a common and possibly even essential library in the facial recognition landscape, and, even in the face of more recent contenders, is a strong candidate for your computer vision and facial recognition or detection framework.
Unlock the full potential of spaCy with this guide to building production-grade text classification pipelines for business data.
We compare 12 AI text summarization models through a series of tests to see how BART text summarization holds up against GPT-3, PEGASUS, and more.
Let’s take a look at what intent classification is in conversational ai and how you can build a GPT-3 intent classification model for conversational ai and chatbot pipelines.
Discover the capabilities of zero-shot object detection, which enables anyone to use a model out-of-the-box without any training and generate production-grade results.
What is facial expression recognition and what SOTA models are being used today in production
Get a simple TensorFlow facial recognition model up & running quickly with this tutorial aimed at using it in your personal spaces on smartphones & IoT devices.
Explore accurate classification algorithms using the latest innovations in deep learning, computer vision, and natural language processing.
Learn what human activity recognition means, how it works, and how it’s implemented in various industries using the latest advances in artificial intelligence.
What is the the SetFit architecture and how does it outperform GPT-3 and other few shot large language models
What is image classification and how we build production level TensorFlow image classification systems for recognizing various products on a retail shelf.
Explore the application of intelligent document processing (IDP) in different industries and dive in-depth on intelligent document pipelines.
How to build an image classification model in PyTorch with a real world use case. How you can perform product recognition with image classification
Let's build a custom CTA generator that you'll actually want to use for your website copy
We’re going to look at how we built a state of the art NLP pipeline for blended summarization and NER to process master service agreements (MDAs) that vary the outputs based on the input document and what is deemed important information.
Get a comprehensive overview of a purchase order vs. invoice, including when businesses use each, what information goes in them, and more.
Learn what Google Shopping categories are used for and how you can automate fitting products to this taxonomy using ai.
Automatically categorize your Shopify store products to the Shopify Product Taxonomy instantly with ai based PIM software
Dive deep into 3-way invoice matching, including how it works, eight benefits for your business, and the problems with doing it manually.
Smart farming using computer vision and deep learning provides the most promising path forward in the slow-moving industry of agriculture.
How we leveraged large language models to build a legal clause rewriting pipeline that generates stronger language and more clarity in legal clauses
Using ai for document information extraction to automate various parts of the loan process.
Apply AI to your favorite sport with this guide. Learn how automated ball tracking can change the game for coaches and players.
Categorize your ecommerce products to the 2021 google product taxonomy tree instantly with our Ai software
Surveying the current landscape of ecommerce automation and how you can use ai to automate huge chunks of your product management.
Classify your product data against an existing product category database or generate categories and tags in seconds using artificial intelligence
Warehouse automation plays a crucial role across your supply chain. Learn about how machine learning and ai software can be integrated into your warehouse automation stack.
4 different NLP methods of summarizing longer input text into different methods such as extractive, abstractive, and blended summarization
iscover an invoice OCR tool that will revolutionize the way you handle invoices. There’s no human intervention needed & a dramatically lower per-invoice cost.
Instead of invoice matching taking upwards of a week, it could take mere seconds with the proper automation solution. Learn more here.
Manual and template-based invoicing are riddled with low accuracy and required human intervention. Learn how to systematically eliminate these issues with the right invoice data capture software.
A complete walkthrough guide on how to use visual search in ecommerce stores to create more sales and real examples of companies already using it.
Automating the extraction of data from invoices can reduce the stress of your accountants by finding inaccuracies, digitizing paper invoices, and more.
How you can use machine learning based data matching to compare data features in a scalable architecture for deduping, record merging, and operational efficiency
Learn how lifetime value or LTV prediction can improve your marketing strategies. Then, discover the best statistical & machine learning models for your predictions.
A deep understanding of how we use gpt-3 and other NLP processes to build flexible chatbot architectures that can handle negotiation, multiple conversation turns, and multiple sales tactics to increase conversions.
The popular HR company O.C. Tanner, which has been in business since 1927 and has over 1500 employees, was looking to research and design two GPT-3 software products to be used as internal tools with their clients. GPT-3 based products can be difficult to outline and design given the sheer lack of publicly available information around optimizing and improving these systems to a production level.
We’ll compare Tableau vs QlikView in terms of popularity, integrations, ease of use, performance, security, customization, and more.
With a context-aware recommender system, you can plan ways to recreate some of the contextual conditions that persuade them to buy more from you.
We’re going to walk through building a production level twitter sentiment analysis classifier using GPT-3 with the popular tweet dataset Sentiment140.
Find out how machine learning in medical imaging is transforming the healthcare world and making it more efficient with three use cases.
Discover ways that machine learning in health care informatics has become indispensable. Review the results of two case studies and consider two key challenges.
Accelerate your growth by pivoting key areas of your business to AI. Your business outcomes will be achieved quicker & you’ll see benefits you didn’t plan for.
We built a GPT-3 based software solution to automate raw data processing and data classification. Our model handles keyword extraction, named entity recognition, text classification | Case Study
We built a custom GPT-3 pipeline for key topic extraction for an asset management company that can be used across the financial domain | Case Study
How you can use GPT-3 to create higher order product categorization and product tagging from your ecommerce listings, and how you can create a powerful product taxonomy system with ai.
5 ways you can use product matching software in ecommerce to create real value that raises your sales metrics and improves your workflow operations.
Data mining and machine learning in cybersecurity enable businesses to ensure an acceptable level of data security 24/7 in highly dynamic IT environments. Learn how data security is getting increasingly automated.
Product recognition software has tremendous potential to improve your profits and slash your costs in your retail business. Find out just how useful it is.
Big data has evolved from hype to a crucial part of scaling your organization in every modern industry. Learn more about how big data is transforming organizations and providing business impacts.
Learn how natural language processing can benefit everybody involved in education from individual students and teachers to entire universities and mass testing agencies.
Here’s how automated data capture systems can benefit your business in some key ways and some real-life examples of what it looks like in practice.
Use these power ai and machine learning tools to create business intelligence in your marketing that pushes your business understanding and analytics past your competition.
We built a custom ML pipeline to automate information extraction and fine tuned it for the legal document domain.
In this practical guide, you'll get to know the principles, architectures, and technologies used for building a data lake implementation.
Find out how machine learning in biology is accelerating research and innovation in the areas of cancer treatment, medical devices, and more.
An enterprise data warehouse (EDW) is a repository of big data for an enterprise. It’s almost exclusive to business and houses a very specific type of data.
Learn how to utilize machine learning to get a higher customer retention rate with this step-by-step guide to a churn prediction model.
Machine learning algorithms are helping the oil and gas industry cut costs and improve efficiency. We'll show you how.
We’ll show you the difference between machine learning vs. data mining so you know how to implement them in your organization.
Here’s why you should use deep learning algorithms in your business, along with some real-world examples to help you see the potential.
Beam search is an algorithm used in many NLP and speech recognition models as a final decision making layer to choose the best output given target variables like maximum probability or next output character.
Best Place For was looking for an image recognition based software solution that could be used to detect and identify different food dishes, drinks, and menu items in images sourced from blogs and Instagram. The images would be pulled from restaurant locations on Instagram and different menu items would be identified in the images. This software solution has to be able to handle high and low quality images and still perform at the highest production level, while accounting for runtime as well as accuracy.
Deep learning recommendation system architectures make use of multiple simpler approaches in order to remediate the shortcomings of any single approach to extracting, transforming and vectorizing a large corpus of data into a useful recommendation for an end user.
Let's take a look at the architecture used to build neural collaborative filtering algorithms for recommendation systems
GPT-3 is one of the most versatile and transformative components that you can include in your framework, application or service. However, sensational headlines have obscured its wide range of capabilities since its launch. Let’s take a look at the ways that companies and researchers are achieving real-world results with GPT-3, and examine the untapped potential of this 'celebrity AI'.
How to get started with machine learning based dynamic pricing algorithms for price optimization and revenue management
Let's take a look at how you can use spaCy, a state of the art natural language processing tool, to build custom software tools for your business that increase ROI and give you data insights your competitors wish they had.
The landscape for AI in ecommerce has changed a lot recently. Some of the most popular products and approaches have been compromised or undermined in a very short time by a new global impetus for privacy reform, and by the way that the COVID-19 pandemic has transformed the nature of retail.
Extremely High ROI Computer Vision Applications Examples Across Different Industries
Building Data Capture Services To Collect High ROI Business Data With Machine Learning and AI
Software packages and Inventory Data tools that you definitely need for all automated warehouse solutions
Inventory automation with computer vision - how to use computer vision in online retail to automate backend inventory processes