I will provide instance semantic and panoptic segmentation solutions

CORE Semantic/Instance/Panoptic SEGMENTATION EXPERTISE:

Foundation Models: SOTA pretrained models SAM, Grounded SAM, SEEM, YOLOE

Transformers: Mask2Former, Segmenter, Swin, SETR

CNNs: U-Net, U²-Net, PSPNet, DeepLab family

ENHANCEMENT & REFINEMENT:

Augmentation: Albumentations, Kornia, weak/pseudo-labeling

Multi-scale fusion: FPN, ASPP for better generalization

Post-Processing: CRF, TTA, ensemble predictions, boundary smoothing morphological ops, component analysis

Backbones: Swin, DINOv2, MobileViT for rich context

Hybrid CNN-Transformer: Faster inference with high accuracy

TECH STACK:

Frameworks: MMSegmentation, Detectron2, custom PyTorch/TF Libraries

OPTIMIZATION & DEPLOYMENT:

TensorRT, OpenVINO, DeepStream

Flask/FastAPI for scalable inference pipelines

DOMAIN EXPERTISE:

R&D/Custom Models: Ensemble segmentation, novel head-backbone pairings, fusion pipelines

Retail/Fashion: Apparel parsing, accessory detection, virtual try-ons

Sports: Player segmentation, pose tracking, action-focused masking

Medical Imaging: Tumor/organs from CT/MRI; semi-supervised histopath slides; DICOM/NIfTI support

Satellite: Multiband segmentation, change detection, GeoTIFF/QGIS workflows