vlm-segmentation-engineering

vlm-segmentation-engineering is a Computer vision skill for AI agents, published by AnastasiyaW in the claude-code-config repository on GitHub. Skillspace rates vlm-segmentation-engineering D (66/100) and it passed the safety scan; the rating is based on static analysis only.

Экспертный скилл по прикладной инженерии VLM, сегментационных моделей и диффузионных архитектур для GPU-деплоя. Используй ВСЕГДА когда речь идёт о: SAM2, SAM3, Florence-2, LLaVA, Grounding DINO, OWLv2, YOLO-World, EdgeTAM — выбор модели, интеграция, pipeline, код; диффузионных моделях — UNet/DiT/Flow/Flux, schedulers, LoRA, AMP, ZeRO/FSDP, text encoders (CLIP/Qwen), VAE, CFG; GPU-деплое — MIG, MPS, torch.compile, TorchAO, Triton, memory optimization, два инстанса на H100; open-vocab сегментации и phrase grounding; part-level labeling и instance masks из текстового промпта; замене/fusion текст-энкодеров; fine-tune/LoRA/DreamBooth диффузионных моделей. Триггеры: SAM, Florence, LLaVA, Grounding DINO, YOLO-World, diffusion, UNet, DiT, Flux, LoRA, scheduler, guidance_scale, VAE, CLIP embeddings, Qwen embedder, MIG, MPS, TorchAO, Triton inference, сегментация по тексту, instance masks, open-vocab detection, text-conditioned segmentation. Do NOT use for pure diffusion-only work without a…

Grade

D

Quality

66 / 100

Safety

Passed

GitHub stars

135

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