mirror of
https://github.com/azaion/ai-training.git
synced 2026-04-23 03:56:35 +00:00
Oleksandr Bezdieniezhnykh
142c6c4de8
- Replaced module-level path variables in constants.py with a structured Pydantic Config class. - Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure. - Fixed bugs related to image processing and model saving. - Enhanced test infrastructure to accommodate the new configuration approach. This refactor improves code maintainability and clarity by centralizing configuration management.
34 lines
2.4 KiB
Markdown
34 lines
2.4 KiB
Markdown
# Problem Statement
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## What is this system?
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Azaion AI Training is an end-to-end machine learning pipeline for training and deploying object detection models. It detects military and infrastructure objects in aerial/satellite imagery — including vehicles, artillery, personnel, trenches, camouflage, and buildings — under varying weather and lighting conditions.
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## What problem does it solve?
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Automated detection of military assets and infrastructure from aerial imagery requires:
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1. Continuous ingestion of human-annotated training data from the Azaion annotation platform
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2. Automated data augmentation to expand limited labeled datasets (8× multiplication)
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3. GPU-accelerated model training using state-of-the-art object detection architectures
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4. Secure model distribution that prevents model theft and ties deployment to authorized hardware
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5. Real-time inference on video feeds with GPU acceleration
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6. Edge deployment capability for low-power field devices
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## Who are the users?
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- **Annotators/Operators**: Create annotation data through the Azaion platform. Their annotations flow into the training pipeline via RabbitMQ.
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- **Validators/Admins**: Review and approve annotations, promoting them from seed to validated status.
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- **ML Engineers**: Configure and run training pipelines, monitor model quality, trigger retraining.
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- **Inference Operators**: Deploy and run inference on video feeds using trained models on GPU-equipped machines.
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- **Edge Deployment Operators**: Set up and run inference on OrangePi5 edge devices in the field.
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## How does it work (high level)?
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1. Annotations (images + bounding box labels) arrive via a RabbitMQ stream from the Azaion annotation platform
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2. A queue consumer service routes annotations to the filesystem based on user role (operator → seed, validator → validated)
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3. An augmentation pipeline continuously processes validated images, producing 8 augmented variants per original
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4. A training pipeline assembles datasets (70/20/10 split), trains a YOLOv11 model over ~120 epochs, and exports to ONNX format
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5. Trained models are encrypted with AES-256-CBC, split into small and big parts, and uploaded to the Azaion API and S3 CDN respectively
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6. Inference clients download and reassemble the model, decrypt it using a hardware-bound key, and run real-time detection on video feeds using TensorRT or ONNX Runtime
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7. For edge deployment, models are exported to RKNN format for OrangePi5 devices
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