Why We Still Need Data Annotation?

Data annotation forms the bedrock for AI's worldview.

As AI technologies still operate in a supervised learning paradigm, AI models like deep neural networks are trained and validated by labeling key features in data samples. This metadata usually manifests as classification tags, bounding boxes, or textual commentary which elucidate the pertinent elements requiring model comprehension. Annotating data via classification, spatial demarcation, tagging, etc., to furnish accurate labels remains instrumental for constructing training datasets in machine learning and continues to be a pivotal task in AI development.

What Makes Quality Data Annotation?

Good Source Data

Foremost, amassing substantial data volume is vital for robust model performance. Beyond attaining baseline quantitative thresholds, the data corpus must also capture multidimensional diversity – for instance, autonomous vehicles must learn from examples traversing freeways, side streets, nighttimes, inclement weather, and more. Thus diverse, well-represented annotated data enables more comprehensive environmental perception. Target classes should also demonstrate balanced distribution at the micro level.

Secondly, the resolution of the raw data itself conveys information from the physical to the digital realm. As the information vessel, source data should reproduce real-world scenes with maximal accuracy. Evaluative metrics here encompass image resolution, radar precision, audio bitrate, video frame rate, etc. Counterintuitively, pristine data alone limits generalizability – for in-cabin vocal interfaces, speech data rich with vehicle/traffic sounds proves more realistic.

Annotation Accuracy

What standards validate annotation accuracy?

Although manual annotation results are called “ground truth”, occasional annotator mistakes are inevitable. Anything that gets rejected in a quality check is classified as an annotation error, even if it’s just one pixel or one point deviating. More granular metrics also distinguish between “image-level accuracy” and “box-level accuracy”. If there is only 1 image in a dataset with a total of 100 boxes, and 1 box is wrong, then the “box-level accuracy” would be 99% but the “image-level accuracy” would be 0%.

Consistency also affects accuracy. Variable interpretation of guidelines also impacts consistency, even given the same definitions, leading to different annotation results for the same data.

Annotation Efficiency

For a given dataset to annotate, how to complete the target with less human time is an important consideration for every AI project.

Ignoring ancillary R&D and infrastructure overheads, halving annotation time for the same data and labelers theoretically doubles efficiency. With labor occupying the highest project share and continually rising, efficiency gains can either increase output for the same budget or reduce costs for the same deliverables. As other expenses progressively decline long-term, labor optimization presents the clearest path to maximizing annotation return on investment. Whether measured in total throughput or cost savings, efficiency quantification must account for the outsized impact of human capital.

Data Labeling Challenges in 2024

Like other data preparation workflows, data annotation (data labeling) work remains contingent on human effort. Lengthy cycles and significant human time required have become a major limiting factor in AI industry development.

Data Preparation Occupies 80% of AI Project Time

Typical AI development can be divided into 1) data preparation including collection, cleaning, annotation, and augmentation, and 2) algorithm implementation spanning training, tuning, and deployment. Data preparation tasks hinging on manual labor consume 80% of this workflow, while model-centric work claims just 20%. Among them, data annotation efficiency impacts the time it takes to put AI projects into practice.

Huge Data Volumes Needed for Model Training

Per Dimensional Research, 72% of respondents believe models require over 100,000 data points to ensure robust performance.

Autonomous driving is a typical application of AI models. As most self-driving perception still utilizes supervised deep learning, which deduces correlations from annotated input-output pairs, massive annotated data is mandatory for trainable models. Intel estimates each fully autonomous vehicle will generate 4,000GB of sensor data daily. However, merely 5% of this proves valuable for training. So with algorithms not pulling far ahead, quality training data coverage and annotation specificity become decisive – but intensely demanding.

Annotation Difficulty Is Increasing

Entering 2024, the penetration rate of Level 2 autonomous passenger cars continues to rise on the market, with the overall market shifting towards L3+. The progress behind autonomous driving is the reducing cost of LiDAR, which also means an explosive increase in 3D point cloud data volumes.

Self-driving perception combines 2D image data and 3D point cloud data. 2D image data captured by cameras is mainly used for 2D object detection, 2D semantic segmentation, and target tracking, involving data annotation tasks like point annotation, line annotation, bounding box annotation, and semantic segmentation. 3D point cloud data captured by LiDARs is mainly used for 3D object detection, 3D semantic segmentation, and 3D target tracking, involving annotation tasks like 3D bounding box annotation for point clouds, 2&3D sensor fusion annotation, and 3D point cloud semantic segmentation.

Newer 4D-BEV fusion annotation incorporates both spatial and temporal dimensions to achieve higher accuracy, but requires significantly more effort to annotate complex dynamic scenes with occlusions and truncations. Point cloud annotation not only requires real-time processing and analysis of lidar return data, but various issues like road curvature, and accumulated wear & tear also bring about distortion problems in shape and reflectivity, posing huge challenges and difficulties for recognition accuracy and annotation efficiency.

Smart Data Annotation: Human-Model Coupling to Address Challenges

Various annotation frustrations have fueled the demand for automation, catalyzing smart data annotation technologies.

Pre-Annotation

As deep learning advances, models now mandate exponential data volume, diversity and refresh rates. To tap potential, systems require multifaceted, heterogeneous training data covering images, video, audio and more. Additionally, continual algorithm iteration per application shifts necessitates frequent data updates.

Thus, manual annotation proves inadequate for accurately handling daily data explosions. This propelled human-model coupling solutions that swiftly annotate (or label) vast datasets. Smart data annotation thereby builds on mature AI models. Pretrained models are used to annotate data automatically, then users or annotation teams review, fine-tune the results, and handle difficult cases that models struggle with.

BasicAI Cloud* exemplifies a platform improving efficiency and tackling data bottlenecks through smart data annotation. Forged over 6 years, BasicAI Cloud*’s toolkit underwent two key phases: first, expanding beyond discrete data types like images or audio to support multifaceted combinations like image-point cloud fusions; second, embedding optimized AI models to furnish semi-automated annotation, averaging 50% time savings. Specifically, pre-annotation handles 2D object detection, target tracking, point cloud segmentation, speech transcription, and more.

Interactive Annotation

Interactive annotation incorporates incremental human feedback (points, boxes, affirmations) to steer model predictions. By narrowing the search space with supplementary constraints, models can infer faster and more accurately.

BasicAI Cloud* manifests this in image segmentation and 3D point cloud tasks. Segmentation annotation (semantic, instance, or panoramic) typically demands numerous clicks to delimit targets – rough polygon contours refine to pixel precision. Unlike binary bounding boxes, full segmentation requires exponentially more inputs, so accurate automation drastically boosts efficiency. For 3D point cloud detection, our interactive tools create precise cuboid bounding boxes in just two clicks, rivaling 2D annotation simplicity.

Integrating Team Workflows to Enable Efficient Human-Machine Coupling

For intensive data tasks like data annotation, teamwork is a paradigm. BasicAI Cloud* is designed with robust industrial-level collaboration systems to facilitate this. Smart data annotation tools integrate into users' workflows, enabling true human-model coupling to maximize quality and efficiency. Teams can run offline inference at workflow initiation or directly during annotation. Additionally, automation technologies can be used for data quality inspection – users can validate or auto-compare in semi-automated ways, saving considerable management overhead.

For algorithm engineers, higher levels of annotation automation mean less manual dependence, improving efficiency while significantly reducing production costs, allowing AI models to be put into action sooner. For annotation businesses, algorithmic assistance not only safeguards accuracy but also guarantees efficiency, implying heightened throughput for the same labor hours.

Related Q&A

With pre-trained models, do we still need manual annotation?

Most existing large models utilize self-supervised pretraining (BERT, GPT, Transformers) to reconstruct text, images, and speech absent labeling. Self-supervised pretraining requires massive data and computing resources, unbearable for many companies. Even during finetuning and prediction, GPUs are often insufficient, implying high sustained costs. One-time annotation costs combined with medium-sized models can be more cost-effective in the long term. For supervised pretraining relying on annotated data, models remain smaller but require massive labeling – manual annotation persists as standard. With human-machine coupling workflows, this benefits AI dev teams. In other words, adopting pre-trained models does not eliminate manual annotation, but rather machines assist entire manual annotation workflows.

How to evaluate smart data annotation accuracy?

Let's define model accuracy first.

For classification, accuracy is binary. Detection and segmentation are slightly more complex. or detection/segmentation, model predictions need not exactly match ground truth annotations if the Intersection over Union (IoU) meets a threshold.

So model accuracy does not directly equal annotation accuracy (though they positively correlate). But from efficiency goals, as long as “identification + modification” costs less than pure manual labor, it remains viable. We tend to use soft annotation accuracy metrics (rather than hard binary accuracy or not metrics) to measure model accuracy.

Key Takeaways

• Data annotation (data labeling) is pivotal for AI, with accuracy and efficiency as key markers.

• Data preparation tasks occupy substantial AI development time. Soaring data demands drive smart data annotation advancement to cut costs.

• BasicAI Cloud* embraces human-model coupling for efficiency – pre-annotation, interactive tools, and integrated workflows.

• Smart data annotation aims not to replace manual work but rather for machines to assist entire manual annotation workflows.

Schedule a Demo to See How BasicAI Cloud* Smart Data Annotation Tools Enable Time and Cost Savings

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* To further enhance data security, we discontinue the Cloud version of our data annotation platform since 31st October 2024. Please contact us for a customized private deployment plan that meets your data annotation goals while prioritizing data security.