7 Steps to Training Datasets for Computer Vision Models in 2023: An In-Depth Guide

Computer vision (CV) is rapidly transforming industries from manufacturing to medicine. But developing accurate CV models requires specially tailored training datasets. This comprehensive guide will walk through the 7 critical steps for building optimized datasets from the ground up.

Why Training Data Matters in Computer Vision

Computer vision relies on machines learning to interpret and understand visual inputs like images and video. CV powers use cases like:

  • Automated quality inspection in factories
  • Assisting radiologists in analyzing medical scans
  • Ringing up purchases in cashier-less retail stores
  • Reading signs and obstacles in self-driving cars

However, Gabriela Csurka, VP of AI at image analysis company Definiens, notes that "A computer vision model is only as good as its training data."

Low-quality or biased data leads to errors and poor performance. But with properly constructed training sets, the same algorithms can achieve remarkable accuracy on real-world tasks.

This makes tailored datasets a prerequisite for successfully deploying CV models. But collecting and preparing image data at scale is complex.

This guide will provide end-to-end best practices for constructing optimized CV training sets from the ground up. Mastering training data is the key to achieving CV excellence.

Step 1: Defining Your Data Requirements

The first step is getting crystal clear on what kind of training data your CV model actually requires. This depends on:

The Type of Model

There are many different types of CV models, each requiring different training approaches:

  • Image classification categorizes images into predefined classes. A model distinguishing between dog and cat photos is a basic example.
  • Object detection identifies and locates specific objects within images, such as pedestrians in a traffic scene.
  • Semantic segmentation precisely outlines objects in images at the pixel level, like vehicles on a road.
  • Instance segmentation goes further by differentiating between multiple objects of the same class, like segmenting every individual car.
  • Image generation creates new synthetic images from scratch, such as generating realistic human faces.

Be sure you understand exactly what kind of model you need for your business goals, and what type of training data it requires. Misalignment leads to wasted effort and poor outcomes.

The Target Objects and Environments

Dr. Senthil Yogamani, VP of AI at automotive perception company VSI Labs, emphasizes the need for "application-specific datasets tuned to the operational design domain."

For example, a defect detection model for an electronics factory needs training images of specific products on the actual assembly lines. An agricultural model needs crops and fields, not cities and highways.

The data should cover the real-world variability the CV model needs to handle, across factors like:

  • Lighting conditions (day vs night)
  • Orientations (angles and poses)
  • Occlusions and obstructions
  • Background variation and clutter
  • Image quality (noise, motion blur)

Insufficient diversity leads to brittle models that fail in the field.

Data Types and Formats

While images and video are the primary data types for computer vision, you can also utilize:

  • Lidar/radar/ultrasound scans
  • Infrared or thermal imagery
  • Point clouds
  • Metadata like timestamps, geolocation, weather, camera parameters

Multimodal data provides useful context and signals that complement the visual inputs.

Be sure your data pipeline, storage, and training frameworks support your required data types and formats. This affects everything from collection to model deployment.

Getting your data requirements right from the start ensures your efforts stay aligned to your real-world objectives.

Step 2: Choosing the Right Collection Methods

Once your specific data needs are defined, the next step is collecting a sufficient volume of high-quality raw images or video.

Common approaches include:

MethodOverview
CrowdsourcingOutsourcing data collection to a distributed human workforce. Fast and scalable but requires robust QA.
In-house captureFirst-party capture using your own resources. Highly customized but expensive.
ScrapingHarvesting public online data like social media or search engines at scale. Useful for secondary data.
Public datasetsLeveraging existing academic datasets. Readily available but limited applicability.
Automated captureUsing specialized hardware like drones, robots or fixed cameras to autonomously gather data. Efficient but requires setup.
Data providersPurchasing labeled datasets from specialist computer vision providers. Cost-effective for common domains like automotive.

When determining the right methods, key considerations include:

  • Domain coverage: Does the approach allow collecting diverse, relevant images covering your target objects and environments?
  • Cost: What is the cost per image? Economies of scale versus intensive setup costs?
  • Speed: How fast can data be gathered to meet project timelines?
  • Privacy: Does the method raise any privacy or compliance concerns?
  • Quality: Will the process yield high quality, usable image data?

Most real-world datasets combine multiple collection methods to assemble the large volumes required. For example, crowdsourcing for scale combined with in-house capture for niche objects.

Underutilized sources like existing corporate archives can also be a goldmine. The key is choosing a cost-effective mix of methods that delivers the diversity, speed and quality needed.

Step 3: Preparing High-Quality Training Data

Simply grabbing raw images is not enough. To train CV models effectively, data needs careful preparation and preprocessing:

Data Quality FactorDescription
DiversityVariety in objects, backgrounds, lighting, angles and more. Teaches models to handle real-world variability.
Annotation accuracyPrecise labels, segmentation or keypoints identifying objects of interest. Enables learning.
Class balanceA balanced distribution of each object class. Prevents bias towards overrepresented classes.
ComprehensivenessCoverage of the full scope expected “in the wild”. Enables generalization.
Image qualityHigh-resolution, sharp images without defects, grain, blur or distortions. Prevents "garbage in, garbage out."

Investing in quality assurance pays dividends in model accuracy. Steps like cropping, deduplication, sorting, labeling and checks are essential.

According to an IBM study, data preparation accounts for up to 80% of time spent on machine learning projects. Don‘t shortchange this vital process.

Step 4: Annotation and Labeling

Examples of labeled images for computer vision showing bounding boxes, segmentation and keypoints.
Image annotation examples (Source: Clickworker)

Annotation is the process of adding labels that teach the CV model what objects are present in images and where to find them. Common approaches include:

  • Bounding boxes: Rectangular regions identifying objects of interest. Useful for localization.
  • Segmentation masks: Precisely outlining objects at the pixel level. Retains shape and size information.
  • Keypoints: Marking strategic points like joint positions or facial landmarks. Useful for tracking movements.
  • Captions: Describing the image content in natural language. Provides contextual understanding.

According to Datasaur, over 70% of model accuracy gains are driven by improved training data annotation and labeling.

For best results:

  • Provide detailed annotation guidelines and quality standards upfront.
  • Choose annotators with experience in your domain for maximum precision.
  • Use specialized annotation tools suited to your data formats.
  • Build in QA processes like double annotation and expert audits.
  • Combine automation and human input for optimal efficiency.

Flawed annotations severely hamper model training, so investing in this step pays dividends.

Step 5: Data Augmentation

Even massive datasets may not cover every needed variation.

Data augmentation artificially expands datasets by applying transformations like:

  • Cropping/rotation/flips
  • Color shifts
  • Blurring/noise injections
  • Distortions and transformations
  • Mixing objects onto new backgrounds

According to recent research by Intel, training set augmentation leads to a 68% reduction in model error rates on average.

This exposes models to new "synthetic" training examples to improve generalization. It is especially useful when your real-world dataset has limitations or gaps.

Augmentation can be applied manually or, more commonly, programmatically by data loaders during training. The key is striking a balance – too much augmentation can also degrade model accuracy.

Step 6: Validating Your Dataset

The last step before training is comprehensively validating your dataset:

  • Split your data: Reserve a portion of your dataset for validation and testing rather than model training. A 70/20/10 split is a common starting point.
  • Cross-validate: Train and test models using different subsets of the data to test for stability. Use techniques like k-fold cross-validation.
  • Test on real-world samples: Check model performance by scoring against manually collected test images or videos representing unseen "in the wild" data.

Flaws like annotation errors, insufficient diversity and class imbalance in datasets only become apparent at this validation stage. It‘s far better to detect and rectify issues using validation sets before proceeding to expensive model training.

Step 7: Maintaining Your Data Over Time

CV model performance can slowly degrade over time as real-world data evolves:

  • New products appear on factory conveyors
  • Road signage and markings change
  • Consumer purchase patterns shift

According to a survey by Algorithmia, 52% of ML models decay within one year.

To combat this:

  • Continuously monitor model accuracy on live data flows. Watch for any drops indicating data drift.
  • Schedule periodic model retraining using fresh datasets collected over recent time periods.
  • Enable continuous learning by feeding newly labeled data back into models.

Well-constructed datasets are not "one and done" – they need ongoing curation, validation and expansion to keep pace with the real world.

Constructing optimized training datasets is a complex undertaking, but vitally important for CV success. Here are the key lessons:

  • Deeply understand your target model architecture, data requirements and use cases upfront.
  • Choose cost-effective collection methods that ensure diversity.
  • Invest heavily in dataset cleaning, preprocessing and quality assurance – it makes up the bulk of project time and impact.
  • Precise labeling and annotation is essential for teaching models effectively.
  • Use data augmentation to expand limited datasets.
  • Rigorously validate and test datasets before proceeding to expensive model training.
  • Continuously monitor model accuracy and update datasets over time.

Following these best practices results in tailored datasets that maximize model accuracy on real-world computer vision tasks in the field. Ready to build your tailored training dataset? Get in touch with our team of experts.

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