Vladmodelsy107karinacustomsets 85 High Quality ((full))

| 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 parameters support , 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