Pikaliov
a243601523
Claude Code skill for publication-quality scientific visualisations: chart, architecture, flowchart, comparison, pipeline. Bundles matplotlib publication defaults (300 DPI, serif font, colorblind palette), NADEZHDA project palette, Mermaid conventions, and ready-to-use templates. Contents: - SKILL.md — behaviour spec (5 types, generation pipeline) - README.md — human-facing entry: when, install, examples - reference/chart_patterns.md — Pareto/bar/radar/heatmap snippets - templates/architecture_nadezhda.md — Mermaid templates for Teacher-Student / LUPI / fusion Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
116 lines
3.4 KiB
Markdown
116 lines
3.4 KiB
Markdown
# Шаблоны архитектурных диаграмм NADEZHDA
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## Mermaid: Полная система Teacher-Student
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````markdown
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```mermaid
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graph TB
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subgraph Training ["ОБУЧЕНИЕ (5 модальностей, облако)"]
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direction TB
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SAT_T[🛰 Satellite RGB] --> DINO[DINOv3-L<br/>~300M frozen, patch 16, RegTokens]
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DRONE_T[🚁 Drone RGB] --> DINO
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SV[📷 Street-View] --> DINO
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DEPTH[🗺 Depth Map<br/>DA V2] --> DINO
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TEXT[📝 Text<br/>MobileCLIP2] --> FILM[Multi-FiLM<br/>γ·F + β]
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FILM --> DINO
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DINO --> TFEAT[Teacher Features]
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TFEAT --> TDESC[Teacher Descriptor<br/>512-dim, L2-norm]
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TFEAT --> TSEG[OV-Seg Head]
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end
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subgraph Inference ["ИНФЕРЕНС (2 модальности, Jetson)"]
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direction TB
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SAT_S[🛰 Satellite RGB] --> FVIT[FastViT-T12<br/>8.5M, weight-shared]
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DRONE_S[🚁 Drone RGB] --> FVIT
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FVIT --> SFEAT[Student Features]
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SFEAT --> CVD[CVD Split]
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CVD --> SDESC[Student Descriptor<br/>512-dim, L2-norm]
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end
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TDESC -.->|L_LUPI: MSE| SDESC
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TFEAT -.->|L_feat: Conv1×1| SFEAT
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TDESC -.->|L_RKD: relational| SDESC
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TSEG -.->|L_seg: CE+KL| SFEAT
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style Training fill:#e8eaf6,stroke:#283593
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style Inference fill:#fff8e1,stroke:#f57f17
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```
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````
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## Mermaid: Progressive Loss Staging
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````markdown
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```mermaid
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gantt
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title Progressive Loss Staging (60 epochs)
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dateFormat X
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axisFormat %s
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section Losses
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L_task (InfoNCE) :active, 0, 60
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L_feat (alignment) :crit, 10, 60
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L_CRD (contrastive repr.) :crit, 10, 60
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L_LUPI (privileged MSE) :done, 20, 60
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L_RKD (relational) :done, 20, 60
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L_seg (segmentation) :done, 20, 60
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L_CVD (disentanglement) :done, 20, 60
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section Phases
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Warmup (L_task only) :milestone, 0, 10
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Ramp-up (+L_feat, L_CRD) :milestone, 10, 20
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Full (all 7 losses) :milestone, 20, 60
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```
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````
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## Mermaid: Student Backbone Stages
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````markdown
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```mermaid
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graph LR
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IN["Input<br/>[B,3,256,256]"] --> S1["Stage 1<br/>[B,32,96,96]"]
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S1 --> S2["Stage 2<br/>[B,64,48,48]"]
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S2 --> BRIDGE["Conv1×1<br/>Bridge"]
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BRIDGE --> S3["Stage 3<br/>[B,128,24,24]"]
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S3 --> S4["Stage 4<br/>[B,256,12,12]"]
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S4 --> POOL["GGeM Pool"]
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POOL --> DESC["Descriptor<br/>[B,512]"]
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S1 ~~~ NOTE1["SOFIA<br/>(DCN blocks)"]
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S3 ~~~ NOTE2["MambaVision<br/>(SSM+ViT)"]
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style S1 fill:#fff3e0,stroke:#e65100
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style S2 fill:#fff3e0,stroke:#e65100
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style S3 fill:#e3f2fd,stroke:#1565c0
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style S4 fill:#e3f2fd,stroke:#1565c0
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style NOTE1 fill:#fff3e0,stroke:none,color:#e65100
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style NOTE2 fill:#e3f2fd,stroke:none,color:#1565c0
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```
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````
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## Mermaid: Data Augmentation Pipeline
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````markdown
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```mermaid
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graph LR
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RAW[Raw RGB<br/>512×512] --> RESIZE[Resize<br/>256×256]
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RESIZE --> DA["Depth Anything V2<br/>Large (335M)"]
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RESIZE --> DSINE["DSINE<br/>(30M)"]
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RESIZE --> SEG["SegFormer-B5<br/>(85M)"]
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DA --> D_OUT["depth.npy<br/>[1,256,256]"]
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DSINE --> N_OUT["normal.npy<br/>[3,256,256]"]
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SEG --> S_OUT["seg.npy<br/>[1,256,256]"]
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RESIZE --> RGB["RGB<br/>[3,256,256]"]
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RGB --> CONCAT["concat_8ch.npy<br/>[8,256,256]"]
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D_OUT --> CONCAT
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N_OUT --> CONCAT
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S_OUT --> CONCAT
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style DA fill:#e8f5e9,stroke:#2e7d32
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style DSINE fill:#fce4ec,stroke:#c62828
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style SEG fill:#e3f2fd,stroke:#1565c0
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```
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````
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