| Group | Sub‑group | Example Parameters (total) | |-------|-----------|----------------------------| | Scene & Environment | Geometry, Lighting, Weather | 22 | | Object & Agent | Category, Pose, Material | 18 | | Camera & Sensor | Focal Length, Noise Model, Distortion | 10 | | Audio | Source, Room Impulse Response, SNR, Codec | 12 | | Time‑Series | Trend, Seasonality, Anomaly Rate, Sampling Rate | 8 | | Label & Annotation | Noise Level, Occlusion, Missing Labels | 5 | | KARINA Extensions | Domain‑specific hooks | Variable (user‑defined) |
All parameters support continuous ranges, categorical sets, or probabilistic distributions (e.g., Gaussian, Uniform, Beta). The engine can sample a parameter grid, a random Latin hypercube, or a user‑defined curriculum. vladmodelsy107karinacustomsets 85 high quality
| Category | Representative Systems | Limitations | |----------|------------------------|-------------| | Image Synthesis | Unity Perception, NVIDIA Deep Learning Synthetic Data (DLSD) | Fixed pipelines, limited scene diversity | | Audio Synthesis | WaveGAN, DiffWave, Google’s AudioSet generators | Poor control over speaker identity, environmental acoustics | | Time‑Series Simulation | SimGAN‑TS, PyTSGen | Limited to a few stochastic processes, no domain‑specific extensions | | Unified Frameworks | SynthDet, SynapseML | Focused on single modality; lack of extensibility | | Group | Sub‑group | Example Parameters (total)
VMS‑K85 builds upon these foundations but distinguishes itself by exposing 85 orthogonal configuration knobs and by allowing community‑driven KARINA modules that encapsulate domain knowledge (e.g., medical imaging phantoms, underwater acoustics, financial market simulators). | Task | Architecture | Training Regime |
| Task | Architecture | Training Regime | |------|--------------|-----------------| | Object Detection | Faster‑RCNN + FPN (ResNet‑50) | 12 epochs, AdamW | | Speech‑to‑Text | Conformer (Transformer‑CNN hybrid) | 200 k steps, SpecAugment | | Anomaly Detection | Temporal Convolutional Network (TCN) | 50 epochs, early stopping | | Medical Classification | DenseNet‑121 | 30 epochs, cosine LR schedule |
All models are trained from scratch on either synthetic or real data, using identical hyper‑parameters.