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In 2019,
EORA began
developing a pipeline for the development and training of neural network models with computer vision (CV) technology.
30 projects
for large companies have been done with its help.
In three years, about
TorchOK:
the latest version of the pipeline is written on the Pytorch Lighting engine.
It was named
All projects use image and video processing:
classification
segmentation
search for similar images
face recognition
Projects
Learn more about projects that are made with TorchOK
Data Lab
Data Lab
Data Lab
Data Lab
Data Lab
Data Lab
For whom
Now the pipeline is available to our customers:
Outsource-developers
in the field of Data Science and CV
Product teams
that don't have their own pipeline
Research groups
in the field of Deep Learning
TorchOK pipeline features
Extensive library of computer models: from classic ResNet to the latest Swin transformer;
Metrics for evaluating computer vision models in TensorBoard and MLflow: classification, segmentation, metrics for finding similar images, for face recognition;
The single interface for loading and unloading computer models;
Convenient "packaging": TorchOK can be run through the Conda environment and in the cloud — using Docker containerizer or in Safe Maker on Amazon Web Services.
The wide selection of ready-made datasets: it is only necessary to prepare your data in the required format (CSV file with annotations and paths to images);
Modern infrastructure. TorchOK runs on machines with CPU, GPU, and also on multiple computers with multiple GPUs. There is support for TPU;
VLAD VINOGRADOV
Technical Director of EORA Data Lab
Our task is to bring “raw” computer models to production. Therefore, TorchOK will always have only the best technical solutions. Not the most accurate in terms of public benchmarks, but the most effective in terms of quality/speed.
Advantages of TorchOK
FOR DEVELOPERS
It's simple
Minimal knowledge in Deep Learning is enough to work in pipeline: prepare the dataset for training, train the neural network model and then transfer it for integration.
It is universal
Usually teams write their own pipeline for each project. We use the TorchOK codebase as the basis, and then modify it within the team.
It is suitable for collaboration
TorchOK can be cloned to work in several projects at the same time, for separate development teams. The errors and improvements found are eliminated and added to a single repository and are visible to everyone.
It is easily customized
You choose the training parameters of the neural network model: which network to use, which data to load, which metrics to count, how many GPUs you need.
FOR BUSINESS PROJECTS
Saving money: you have the finished result of many years of EORA work in your hands
Constantly updated database of neural network models
Only the best: we save that really works
Saving time: you can immediately start training your neural network
The most modern solutions in a simple "package"
Quality assurance
Yearly improvements since 2019
The current version is the third
Respected by the research community
Selection of the best technical solutions and programs
Team
Vyacheslav Shultz
Vladislav Vinogradov
Development of critical library blocks, integration of neural network architectures
Library core development, packaging, management
Correction of critical errors
Rashid Bayazitov
Adding of the new task types support
Aelita Shaikhutdinova
Roman Bogachev
Konstantin Kubrak
Transition to permanent support of key neural networks while maintaining the same type of structure: backbone, neck, head, hat
The ability to upload models to different frameworks: TensorRT, ONNX, OpenVINO
Ensuring compatibility of different frameworks and layers of neural network models
Selection of models in terms of quality and speed