Model generation & confidence score of 98%
Model prediction speed 10ms - 1s
4x – 6x Faster than manual labelling
6x – 8x Cost saving
01. Autonomous Vehicles
03. Security & Surveillance
06. Medical Imaging
08. Retail Automation
Why does DigitSquare matter?
DigitSquare is an end-to-end Saas based platform for annotating, training, and automating the computer vision pipeline.
Are we saving the customer – time? Money? People resources?
We are saving time, money and human effort in terms of annotating.
What is Annotation/Labeling?
Annotation is the technique through which we label data so as to make objects recognizable by machines.
Different types of labeling DigitSquare supports?
Rectangle, Polygon, Point and Line.
What all the industries you serve?
Any industry requirement specific to Image/Video processing use-cases.
Is DigitSquare a free tool?
Not a free tool, licensed tool.
Installation required or we can open in browser?
How quick/fast and accurate?
Depends on the Training data set and accurate annotations.
DigitSquare subscription cost?
- Basic -> $10,000
- Advanced -> $20,000
- Enterprise -> $30,000
- Additional cost for Auto Label/Model Generation Hours -> depending on the usage or requirement needs.
Is coding required or anyone can access?
Tool can be accessed only if the license is purchased, Coding is not required, this tool is built for ML Engineers.
How is my data handled on DigitSquare?
DigitSquare complies to GDPR.
How it is used to build AI models?
- Using annotated data sets, if the data is not annotated as well, there is an option to auto label, set hyperparameters and generate models.
- Models generated using globally available standard formats.
How machine learning and deep learning works for image annotation.
- Refer the same Answers as above.
- Using annotated data sets, if the data is not annotated as well, there is an option to auto label, set hyper parameters and generate models.
- Models generated using globally available standard formats. (Pytorch, TensorFlow, ONNX etc).
What is synthetic data? Adv/dis adv of synthetic data? Types
- Synthetic Image generation is the creation of artificially generated images that look as realistic as real images.
- ML Engineers often require highly quantitative accurate, and diverse datasets to train and build accurate ML models.
- Synthetic data helps in reducing the costs of data collection and data labelling. In addition to lowering costs,
- synthetic raw data helps address privacy issues associated with sensitive real-world data.
- Types of Synthetic Data
- Text data: Synthetic data can be artificially generated text in natural language processing (NLP) applications.
- Tabular data: Tabular synthetic data refers to artificially generated data like real-life data logs or tables useful for classification or regression tasks.
- Media: Synthetic data can also be synthetic video, image, or sound to be used in computer vision applications.