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💥 Deep Dive: SuperAnnotate, End-to-End Platform for Building and Managing SuperData, the Ground Truth of AI

Annotated datasets form the foundation for supervised learning, one of the most popular and widely used types of ML algorithms. The accuracy of a trained machine learning algorithm relies heavily on the quality of the data labels, making the creation of ground truth one of the most important steps in developing these algorithms. While the collection of raw data is often the easiest part of building a dataset, adding context to the raw data with annotation takes time and is very tedious.  

Data annotation is generally outsourced to annotation services or crowdsourced to freelancers who clean the data and process it to form a dataset ready to be analyzed. While crowdsourcing is an efficient way to label raw data, they often bring the risk of quality loss in the form of incorrect annotations, bringing forth the requirement of AI in data management. 

AI data management helps create high-quality datasets by automating a large part of the annotation process and performing routine quality checks to prevent errors. Furthermore, AI helps clean large datasets by identifying and removing duplicated and noisy data in just a fraction of the time otherwise needed by human annotators. 

We keep covering data annotation solutions, and today we want to overview SuperAnnotate, the end-to-end platform to annotate, version, and manage ground truth data for AI. Let us dive into SuperAnontate’s platform to understand its capabilities better.

Dataset creation 

SuperAnnotate makes dataset creation a seamless process. Users can add image, video, and text files directly to the platform as well as attach them through secure cloud integrations using their AWS, GCP, or Azure storage. 

Users can create annotations on the platform or import them via Python SDK using the JSON format. SuperAnnotate also supports popular dataset formats such as COCO, YOLO, and Pascal-VOC.

The team can access the Python SDK using a token, an authentication key generated by the team owner. In addition, annotated data can be exported and shared in multiple formats allowing users to build their own neural networks while simultaneously offering data integration with SuperAnnotate’s networks. 

Annotation tools 

SuperAnnotate provides advanced annotation tools to annotate image, video, and text datasets. Image annotation tools like bounding boxes, polygons, ellipses, and keypoints are available in almost all image annotation toolboxes in the market. However, SuperAnnotate’s advanced tools make manual annotation and the quality assurance process at least 2x faster than other open-source tools or professionally-managed software. 

Quality management 

SuperAnnotate has a comprehensive quality review system. Items pass through a multi-level QA system where different project members review them to guarantee quality results. 

Project admins can approve or disapprove items in their entirety or specific objects and tags within them. This lets annotators know what exactly they need to review. For more seamless communication, project members can communicate with each other using an integrated chat feature. 

Data curation and versioning 

With SuperAnnotate, users can monitor a dataset’s analytics, create different versions of the same dataset, and compare models. 

Dataset analytics: The analytics dashboard gives an overview of class and attribute distributions, user performance, and project progress. It is particularly useful for organizations that outsource their annotation projects. 

Dataset versioning: Users can create multiple versions of the same dataset and share datasets. They can also visualize the data to identify recurring annotation biases.  

Model comparison: SuperAnnotate allows users to simultaneously compare the outputs from multiple trained models to help users understand specific subsets where their models make wrong predictions. This enables users to understand which category of annotations requires more representation and select the next best subset to improve model accuracy continuously. Additionally, a qualitative view of the network predictions assesses model performance with regular quantitative metrics.  

Model training and deployment 

SuperAnnotate’s Python SDK allows model training with one click within the platform, making it possible to integrate training and prediction calls within the Python pipeline. In addition to custom models trainable on the platform, many pre-trained models are available for transfer learning on new datasets with many hyper-parameters to hand tune. SuperAnnotate’s SDK makes it possible to deploy models trained on the platform to popular independent devices like the Jetson series or the OpenCV AI Kit (OAK). Towards this direction, there are a number of tutorials and Google Colab notebooks that demonstrate the entire pipeline from training to deployment here. 

The AI data annotation solution 

Last but not least, it is often impossible to create high-quality training datasets without professional annotation teams that can fully understand complex instructions and eliminate the possibility of a “garbage in, garbage out” problem for your AI. 

SuperAnnotate provides a scalable solution for annotations of various kinds with a particular focus on image, video, and text datasets. Besides providing a plethora of annotation tools like polylines, polygons, boxes, ellipses, etc., for pixel-perfect annotation, SuperAnnotate offers a marketplace of vetted annotators and AI solutions experts to oversee any annotation requirements of their clients. 

Multiple data upload methods are possible, including through the web interface and cloud-based platforms with annotation upload supported through the Python SDK. SuperAnnotate provides a data curation feature to help reduce redundancy and bias in datasets and provides effective round-the-clock maintenance through on-time data quality monitoring. 

The other thing about SuperAnnotate is that it does not limit itself to data annotation and dataset management. It also provides AI services to integrate dataset creation and model training, increasing the efficiency of the training pipeline and significantly reducing the client workload.  

Conclusion 

Machine learning has become an integral part of our lives, making things easier and more convenient. With the performance of supervised machine learning models relying heavily on the quality of annotated data used, an unprecedented rise in the use of data annotation services is observed. The recent growth of many data annotation services providing quality annotations like SuperAnnotate seems to be the impetus machine learning algorithms need to step out from a controlled research environment and apply to real-world scenarios.