Initial commit — scientific-viz skill

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>
2026-05-04 11:13:08 +03:00
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--- a/.gitignore
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+# Python build artifacts
+__pycache__/
+*.py[cod]
+*$py.class
+*.egg-info/
+*.egg
+.eggs/
+build/
+dist/
+
+# Virtual environments
+.venv/
+venv/
+env/
+ENV/
+.python-version
+
+# Coverage / testing
+.coverage
+.coverage.*
+htmlcov/
+.tox/
+.nox/
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+.dmypy.json
+
+# IDE / OS
+.idea/
+.vscode/
+!.vscode/settings.json
+!.vscode/launch.json
+!.vscode/extensions.json
+*.swp
+*.swo
+*~
+.DS_Store
+Thumbs.db
+desktop.ini
+
+# Jupyter
+.ipynb_checkpoints/
+
+# Secrets — never commit
+.env
+.env.local
+*.key
+*.pem
+credentials.json
+
+# Temp
+*.log
+*.tmp
+*.bak
+*.backup
+*.orig
+~$*
--- a/README.md
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+# scientific-viz
+
+Claude Code skill for **publication-quality scientific visualizations** — charts (matplotlib/seaborn), neural-network architecture diagrams (matplotlib patches *or* Mermaid), block-scheme flowcharts, training-pipeline diagrams, and benchmark comparison figures. Tuned for the CVGL / NADEZHDA project but reusable for any deep-learning paper.
+
+> Behaviour spec — see [`SKILL.md`](SKILL.md). This file is the human-facing entry point.
+
+## When to use
+
+- Need a figure for a paper / report / slide deck and want it consistent with project style.
+- Want a Pareto plot (params vs R@1), grouped bar, radar, heatmap, or training curve.
+- Need a Teacher–Student / multi-modal fusion architecture diagram.
+- Need a Mermaid flowchart embeddable in Obsidian for an LUPI / KD pipeline.
+- Want a quick comparison figure across CVGL benchmarks (University-1652, GeoText-1652, GTA-UAV, VisLoc).
+
+## Invocation
+
+```text
+/scientific-viz <type> "<description>"
+```
+
+Where `<type> ∈ {chart, architecture, flowchart, comparison, pipeline}`.
+
+**Examples**
+
+```text
+/scientific-viz chart        "R@1 comparison across methods on University-1652"
+/scientific-viz architecture "Teacher-Student LUPI pipeline"
+/scientific-viz flowchart    "Training progressive staging 3 phases"
+/scientific-viz comparison   "Backbone candidates: params vs R@1"
+/scientific-viz pipeline     "LUPI distillation flow with 7 losses"
+```
+
+## Five visualisation types
+
+| Type | Output | Use for |
+|------|--------|---------|
+| `chart` | Python script (`.py`) **+** `.png` (300 DPI) **+** `.pdf` (vector) | metric comparisons, ablations, distributions, training curves |
+| `architecture` | matplotlib patches Python (complex) **or** Mermaid (Obsidian-embeddable) | Teacher/Student, backbone stages, fusion modules, head designs |
+| `flowchart` | Mermaid (`graph TD` / `graph LR`) + optional Python | training/inference pipeline, experimental workflow, data processing |
+| `comparison` | Python `.py` + `.png/.pdf` | Pareto front, grouped bar, radar/spider, heatmap |
+| `pipeline` | Mermaid + matplotlib | LUPI distillation flow, augmentation chain, edge deployment |
+
+## Publication-quality defaults (charts)
+
+Every generated `chart` / `comparison` / `pipeline` Python script starts with this rcParams block:
+
+```python
+import matplotlib
+matplotlib.rcParams.update({
+    'font.family': 'serif',
+    'font.size': 11,
+    'axes.labelsize': 12,
+    'axes.titlesize': 13,
+    'legend.fontsize': 10,
+    'xtick.labelsize': 10,
+    'ytick.labelsize': 10,
+    'figure.dpi': 300,
+    'savefig.dpi': 300,
+    'savefig.bbox': 'tight',
+    'axes.grid': True,
+    'grid.alpha': 0.3,
+})
+```
+
+Hard requirements baked in:
+
+- ≥ 300 DPI, vector PDF + raster PNG saved side-by-side.
+- Times New Roman / DejaVu Serif, ≥ 10 pt.
+- Colorblind-safe palette (Okabe-Ito or `tab10`).
+- English axis labels for publications, optional Russian for internal reports.
+- Light-gray grid (`alpha=0.3`), tight layout, no clipped labels.
+
+## NADEZHDA project palette (consistent across figures)
+
+| Component | Colour | Hex |
+|-----------|--------|-----|
+| Teacher | Deep blue | `#1f77b4` |
+| Student | Orange | `#ff7f0e` |
+| Satellite modality | Green | `#2ca02c` |
+| Drone modality | Red | `#d62728` |
+| Street-view | Purple | `#9467bd` |
+| Depth | Brown | `#8c564b` |
+| Text | Pink | `#e377c2` |
+| Loss / gradient | Gray | `#7f7f7f` |
+| Edge / Jetson | Teal | `#17becf` |
+
+## Mermaid conventions (architecture / flowchart / pipeline)
+
+- `graph TD` — vertical flow (Teacher → Student stack).
+- `graph LR` — horizontal pipeline (input → backbone → neck → heads).
+- Thick arrows = main data flow · dashed arrows = gradient / loss signals.
+- Loss labels on arrows: `L_task`, `L_LUPI`, `L_feat`, `L_RKD`, `L_seg`, `L_CVD_MI`, `L_CVD_Recon`.
+- `style` / `classDef` to mark Teacher (blue), Student (orange), shared (green).
+
+Ready-to-use templates: [`templates/architecture_nadezhda.md`](templates/architecture_nadezhda.md).
+
+## Standard tensor shapes (for architecture annotations)
+
+```
+Input:      [B, 3, 256, 256]
+Stage 1:    [B,  32,  96,  96]
+Stage 2:    [B,  64,  48,  48]
+Stage 3:    [B, 128,  24,  24]
+Stage 4:    [B, 256,  12,  12]
+Descriptor: [B, 512]   L2-normalized
+```
+
+## Installation
+
+Drop this repo into your vault's Claude Code skills directory:
+
+```bash
+git clone https://git.lissad.keenetic.name/Pikaliov/scientific-viz.git \
+    .claude/skills/scientific-viz
+```
+
+Or as a submodule:
+
+```bash
+git submodule add https://git.lissad.keenetic.name/Pikaliov/scientific-viz.git \
+    .claude/skills/scientific-viz
+```
+
+Claude Code picks up the skill on the next session. Verify with `/help` — `/scientific-viz` should appear in the user-invocable list.
+
+**Runtime dependencies** (charts & comparison types):
+
+```bash
+pip install matplotlib seaborn numpy
+```
+
+Mermaid output requires no Python — the diagram block embeds directly into Obsidian, GitHub-flavoured Markdown, or Quarto.
+
+## File layout
+
+```
+scientific-viz/
+├── README.md                                — this file
+├── SKILL.md                                 — behaviour spec (5 types, generation pipeline)
+├── reference/
+│   └── chart_patterns.md                    — canonical Pareto/grouped-bar/radar/heatmap snippets
+└── templates/
+    └── architecture_nadezhda.md             — Mermaid templates for Teacher-Student / LUPI / fusion
+```
+
+## Hard constraints
+
+- ❌ Никаких внешних API (OpenRouter, Gemini и т. п.) — только локальные библиотеки.
+- ❌ Никакой растровой генерации архитектур через `PIL.ImageDraw` — только matplotlib `patches` или Mermaid.
+- ✅ Всегда сохранять и `.py` скрипт, и результат (`.png`/`.pdf`) — рядом, рядом с заметкой / в `attachments/figures/`.
+- ✅ Каждое значение в графике должно иметь источник (комментарий в коде или caption под фигурой).
+- ✅ Для Obsidian — Mermaid-блок встраивается прямо в `.md`, без ссылок на внешние файлы.
+- ✅ Для публикаций — английский язык подписей; для внутренних отчётов — RU допустим.
+
+## Worked example
+
+```text
+/scientific-viz comparison "Pareto front: backbone params vs R@1 on University-1652, mark NADEZHDA target at 8.5M"
+```
+
+Expected output:
+
+- `pareto_params_r1.py` — full matplotlib script with palette, grid, edge-budget shaded region, NADEZHDA marker as star.
+- `pareto_params_r1.png` (300 DPI) + `pareto_params_r1.pdf` (vector) saved next to the script.
+- Inline annotation list (Sample4Geo, VimGeo, QDFL, MobileGeo, (MGS)², NADEZHDA target).
+- Source comment at the top of the script linking to `1_lit_research/СИНТЕЗ_всех_статей_для_LUPI_CVGL.md` rows.
+
+See [`reference/chart_patterns.md`](reference/chart_patterns.md) for the canonical pattern this expands from.
+
+## Allowed tools
+
+`Read`, `Write`, `Edit`, `Bash`, `Glob`, `Grep` — the skill writes Python scripts and Mermaid blocks directly into the vault, then optionally executes the script via `Bash` to produce the rendered `.png`/`.pdf`.
+
+## Related skills
+
+- `/analyze-paper` — surfaces the numbers this skill plots.
+- `/synthesize-review` — produces the cross-paper tables that feed `comparison` figures.
+- `/generate-hypothesis` — the hypotheses whose evidence is plotted.
--- a/SKILL.md
+++ b/SKILL.md
@@ -0,0 +1,181 @@
+---
+name: scientific-viz
+description: "Generate publication-quality scientific visualizations: charts (matplotlib/seaborn), neural network architecture diagrams, block-scheme flowcharts (mermaid), training pipeline diagrams, and benchmark comparison tables. Use when the user needs any visual figure for papers, reports, or presentations."
+argument-hint: "[type: chart|architecture|flowchart|comparison|pipeline] [description]"
+user-invocable: true
+allowed-tools: Read Write Edit Bash Glob Grep
+---
+
+# Научная визуализация для CVGL
+
+Генерация визуализаций для научных публикаций, отчётов и презентаций проекта NADEZHDA.
+
+## Входные данные
+
+- `$ARGUMENTS` — тип визуализации + описание
+- Примеры:
+  - `/scientific-viz chart "R@1 comparison across methods on University-1652"`
+  - `/scientific-viz architecture "Teacher-Student LUPI pipeline"`
+  - `/scientific-viz flowchart "Training progressive staging 3 phases"`
+  - `/scientific-viz comparison "Backbone candidates: params vs R@1"`
+
+## Типы визуализаций
+
+### 1. `chart` — Графики и диаграммы (matplotlib + seaborn)
+
+Для: сравнение метрик, аблации, распределения, training curves.
+
+**Выход:** Python-скрипт `.py` + сохранённый `.png`/`.pdf`
+
+Обязательные требования:
+- Разрешение ≥ 300 DPI
+- Шрифт: Times New Roman или DejaVu Serif, ≥ 10pt
+- Colorblind-safe палитра (Okabe-Ito или tab10)
+- Подписи осей на **английском** (для публикаций)
+- Легенда без перекрытия данных
+- Grid: light gray, alpha=0.3
+- Tight layout, без обрезки подписей
+- Формат сохранения: PNG (300 DPI) + PDF (vector)
+
+Стиль matplotlib (использовать в начале каждого скрипта):
+```python
+import matplotlib.pyplot as plt
+import matplotlib
+matplotlib.rcParams.update({
+    'font.family': 'serif',
+    'font.size': 11,
+    'axes.labelsize': 12,
+    'axes.titlesize': 13,
+    'legend.fontsize': 10,
+    'xtick.labelsize': 10,
+    'ytick.labelsize': 10,
+    'figure.dpi': 300,
+    'savefig.dpi': 300,
+    'savefig.bbox': 'tight',
+    'axes.grid': True,
+    'grid.alpha': 0.3,
+})
+```
+
+Шаблоны: см. [reference/chart_patterns.md](reference/chart_patterns.md)
+
+### 2. `architecture` — Архитектуры нейросетей
+
+Для: Teacher/Student диаграммы, backbone stages, fusion modules, head designs.
+
+**Два варианта выхода:**
+
+**A) Python (matplotlib + patches/arrows)** — для сложных кастомных диаграмм:
+- Прямоугольники = слои/модули
+- Стрелки = потоки данных
+- Цвета = разные модальности/компоненты
+- Аннотации: размерности тензоров, число параметров
+- Используй `matplotlib.patches.FancyBboxPatch` для красивых блоков
+
+**B) Mermaid** — для встраивания в Obsidian:
+```mermaid
+graph TD
+    A[Satellite RGB] --> B[DINOv2-L Encoder]
+    C[Drone RGB] --> B
+    D[Depth Map] --> E[CNN Encoder]
+    ...
+```
+
+Шаблоны: см. [templates/architecture_nadezhda.md](templates/architecture_nadezhda.md)
+
+### 3. `flowchart` — Блок-схемы процессов
+
+Для: training pipeline, inference pipeline, experimental workflow, data processing.
+
+**Выход:** Mermaid-диаграмма (встраиваемая в Obsidian) + опционально Python-версия
+
+Mermaid-конвенции:
+- `graph TD` для вертикальных flow
+- `graph LR` для горизонтальных pipeline
+- Цвета через `style` или `classDef`
+- Подписи на английском (для публикаций) или русском (для отчётов)
+
+### 4. `comparison` — Сравнительные визуализации
+
+Для: scatter plot params-vs-accuracy, grouped bar chart методов, radar chart по критериям.
+
+Паттерны:
+- **Pareto front:** params (x) vs R@1 (y), маркеры = разные методы, размер = FLOPs
+- **Grouped bar:** методы × датасеты, hatching для edge/cloud
+- **Radar/spider:** критерии (accuracy, speed, size, generalization, robustness)
+- **Heatmap:** метод × датасет → R@1 значения
+
+### 5. `pipeline` — Диаграммы пайплайна
+
+Для: LUPI distillation flow, data augmentation pipeline, edge deployment chain.
+
+Специальная нотация:
+- Thick arrows = main data flow
+- Dashed arrows = gradient flow / loss signals
+- Color: blue = Teacher, orange = Student, green = shared
+- Labels on arrows: loss names (L_task, L_LUPI, L_feat, L_RKD)
+
+## Процесс генерации
+
+### Шаг 1: Определи тип и параметры
+
+Из `$ARGUMENTS` извлеки:
+- Тип: chart / architecture / flowchart / comparison / pipeline
+- Что визуализировать
+- Целевой формат: Obsidian (mermaid) / публикация (matplotlib) / оба
+- Язык подписей: EN (default для публикаций) / RU (для отчётов)
+
+### Шаг 2: Найди данные
+
+Если визуализация требует числовых данных:
+- Поищи в `1_lit_research/СИНТЕЗ_всех_статей_для_LUPI_CVGL.md`
+- Или в конкретных конспектах `1_lit_research/6_cvgl/P{N}_*.md`
+- Или спроси пользователя
+
+### Шаг 3: Сгенерируй код
+
+- Python-скрипт: сохранить в `3_work/` или `attachments/figures/`
+- Mermaid: вставить inline в .md файл
+- Всегда указывай: `plt.savefig("filename.png", dpi=300, bbox_inches='tight')`
+
+### Шаг 4: Добавь аннотации
+
+Каждая визуализация должна иметь:
+- Заголовок (title)
+- Подписи осей (xlabel, ylabel)
+- Легенду (если >1 series)
+- Источник данных (caption или комментарий в коде)
+
+## Специфика проекта NADEZHDA
+
+### Цветовая схема компонентов
+
+| Компонент | Цвет | Hex |
+|:----------|:-----|:----|
+| Teacher | Deep blue | `#1f77b4` |
+| Student | Orange | `#ff7f0e` |
+| Satellite modality | Green | `#2ca02c` |
+| Drone modality | Red | `#d62728` |
+| Street-view | Purple | `#9467bd` |
+| Depth | Brown | `#8c564b` |
+| Text | Pink | `#e377c2` |
+| Loss / gradient | Gray | `#7f7f7f` |
+| Edge / Jetson | Teal | `#17becf` |
+
+### Стандартные размерности тензоров
+
+```
+Input:     [B, 3, 256, 256]
+Stage 1:   [B, 32, 96, 96]
+Stage 2:   [B, 64, 48, 48]
+Stage 3:   [B, 128, 24, 24]
+Stage 4:   [B, 256, 12, 12]
+Descriptor: [B, 512] L2-normalized
+```
+
+## Ограничения
+
+- НЕ используй внешние API (OpenRouter, Gemini) — только локальные библиотеки
+- НЕ генерируй растровые диаграммы архитектур через PIL/ImageDraw — используй matplotlib patches или mermaid
+- Всегда сохраняй и .py скрипт, и результат (.png/.pdf)
+- Для Obsidian: mermaid блоки встраиваются напрямую в .md
--- a/reference/chart_patterns.md
+++ b/reference/chart_patterns.md
@@ -0,0 +1,171 @@
+# Шаблоны визуализаций для CVGL
+
+## 1. Pareto Front: Params vs R@1
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+methods = {
+    'Sample4Geo':  (88.5,  92.65),
+    'VimGeo':      (50.87, 96.19),
+    'QDFL':        (5.0,   95.00),
+    'MobileGeo':   (28.57, 97.15),
+    '(MGS)²':      (100.0, 97.50),
+    'NADEZHDA':    (8.5,   None),  # target
+}
+
+fig, ax = plt.subplots(figsize=(8, 5))
+for name, (params, r1) in methods.items():
+    if r1 is None:
+        ax.scatter(params, 90, marker='*', s=200, c='#ff7f0e', zorder=5)
+        ax.annotate(f'{name}\n(target)', (params, 90), fontsize=9, ha='center', va='bottom')
+    else:
+        ax.scatter(params, r1, s=100, zorder=4)
+        ax.annotate(name, (params, r1), fontsize=9, textcoords='offset points', xytext=(5, 5))
+
+ax.set_xlabel('Parameters (M)')
+ax.set_ylabel('R@1 (%) on University-1652')
+ax.set_title('Accuracy vs Model Size: CVGL Methods')
+ax.axvspan(0, 10, alpha=0.1, color='green', label='Edge budget (≤10M)')
+ax.legend()
+plt.savefig('pareto_params_r1.png', dpi=300, bbox_inches='tight')
+plt.savefig('pareto_params_r1.pdf', bbox_inches='tight')
+```
+
+## 2. Grouped Bar: R@1 Across Datasets
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+methods = ['Sample4Geo', 'VimGeo', 'GeoDTR+', 'CAMP', 'CGSI']
+datasets = ['Univ-1652', 'CVUSA', 'CVACT-test']
+data = np.array([
+    [92.65, 98.68, 75.0],   # Sample4Geo
+    [0,     96.19, 81.69],  # VimGeo
+    [94.67, 95.43, 0],      # GeoDTR+
+    [94.46, 98.97, 0],      # CAMP
+    [95.45, 0,     0],      # CGSI
+])
+
+x = np.arange(len(methods))
+width = 0.25
+fig, ax = plt.subplots(figsize=(10, 5))
+for i, ds in enumerate(datasets):
+    mask = data[:, i] > 0
+    bars = ax.bar(x[mask] + i * width, data[mask, i], width, label=ds)
+
+ax.set_ylabel('R@1 (%)')
+ax.set_xticks(x + width)
+ax.set_xticklabels(methods, rotation=15, ha='right')
+ax.legend()
+ax.set_ylim(70, 102)
+plt.savefig('r1_comparison.png', dpi=300, bbox_inches='tight')
+```
+
+## 3. Training Curve with Loss Components
+
+```python
+import matplotlib.pyplot as plt
+import numpy as np
+
+epochs = np.arange(60)
+# Simulated progressive staging
+l_task = 1.5 * np.exp(-0.03 * epochs) + 0.2
+l_lupi = np.where(epochs < 10, 0, np.where(epochs < 20,
+    0.5 * (epochs - 10) / 10, 0.5)) * np.exp(-0.02 * (epochs - 10).clip(0))
+l_feat = np.where(epochs < 10, 0, 0.3 * np.exp(-0.02 * (epochs - 10)))
+l_total = l_task + l_lupi + l_feat
+
+fig, ax = plt.subplots(figsize=(8, 4))
+ax.plot(epochs, l_task, label='$L_{task}$ (InfoNCE)', linewidth=2)
+ax.plot(epochs, l_lupi, label='$L_{LUPI}$ (MSE)', linewidth=2, linestyle='--')
+ax.plot(epochs, l_feat, label='$L_{feat}$ (alignment)', linewidth=2, linestyle=':')
+ax.plot(epochs, l_total, label='$L_{total}$', linewidth=2.5, color='black', alpha=0.7)
+
+ax.axvspan(0, 10, alpha=0.08, color='blue', label='Warmup')
+ax.axvspan(10, 20, alpha=0.08, color='orange', label='Ramp-up')
+ax.axvspan(20, 60, alpha=0.05, color='green', label='Full')
+
+ax.set_xlabel('Epoch')
+ax.set_ylabel('Loss')
+ax.set_title('Progressive Loss Staging (NADEZHDA)')
+ax.legend(ncol=2, fontsize=9)
+plt.savefig('training_curve.png', dpi=300, bbox_inches='tight')
+```
+
+## 4. Architecture Block Diagram (matplotlib patches)
+
+```python
+import matplotlib.pyplot as plt
+import matplotlib.patches as mpatches
+
+fig, ax = plt.subplots(figsize=(14, 6))
+ax.set_xlim(0, 14)
+ax.set_ylim(0, 6)
+ax.axis('off')
+
+def add_block(ax, xy, w, h, label, color, sublabel=''):
+    box = mpatches.FancyBboxPatch(xy, w, h, boxstyle='round,pad=0.1',
+                                   facecolor=color, edgecolor='black', linewidth=1.5, alpha=0.85)
+    ax.add_patch(box)
+    ax.text(xy[0] + w/2, xy[1] + h/2, label, ha='center', va='center',
+            fontsize=10, fontweight='bold')
+    if sublabel:
+        ax.text(xy[0] + w/2, xy[1] + 0.15, sublabel, ha='center', va='bottom',
+                fontsize=7, color='gray')
+
+def add_arrow(ax, start, end, label=''):
+    ax.annotate('', xy=end, xytext=start,
+                arrowprops=dict(arrowstyle='->', lw=1.5, color='#333'))
+    if label:
+        mid = ((start[0]+end[0])/2, (start[1]+end[1])/2 + 0.15)
+        ax.text(*mid, label, fontsize=7, ha='center', color='#555')
+
+# Teacher side
+add_block(ax, (0.5, 4), 2, 1.2, 'DINOv2-L\n(frozen+LoRA)', '#1f77b4', '304M params')
+add_block(ax, (0.5, 2), 2, 1.2, 'Multi-FiLM\nFusion', '#9467bd', 'text→visual modulation')
+add_block(ax, (0.5, 0.3), 2, 1.2, 'Teacher\nDescriptor', '#1f77b4', '512-dim')
+
+# Student side
+add_block(ax, (5, 4), 2, 1.2, 'FastViT-T12\n(shared)', '#ff7f0e', '8.5M params')
+add_block(ax, (5, 0.3), 2, 1.2, 'Student\nDescriptor', '#ff7f0e', '512-dim')
+
+# Distillation arrows
+add_arrow(ax, (2.5, 0.9), (5, 0.9), '$L_{LUPI}$')
+add_arrow(ax, (2.5, 4.6), (5, 4.6), '$L_{feat}$')
+
+ax.set_title('NADEZHDA: Teacher-Student Architecture', fontsize=14, fontweight='bold')
+plt.savefig('nadezhda_architecture.png', dpi=300, bbox_inches='tight')
+```
+
+## 5. Mermaid: LUPI Distillation Pipeline
+
+````markdown
+```mermaid
+graph LR
+    subgraph Teacher ["Teacher (356M, cloud)"]
+        S1[Satellite RGB] --> TE[DINOv2-L + LoRA]
+        D1[Drone RGB] --> TE
+        SV[Street-View] --> TE
+        DP[Depth Map] --> TE
+        TX[Text Desc.] --> FM[Multi-FiLM]
+        FM --> TE
+        TE --> TD[Teacher Descriptor 512-dim]
+    end
+
+    subgraph Student ["Student (8.5M, edge)"]
+        S2[Satellite RGB] --> SE[FastViT-T12]
+        D2[Drone RGB] --> SE
+        SE --> SD[Student Descriptor 512-dim]
+    end
+
+    TD -->|L_LUPI: MSE| SD
+    TE -->|L_feat: Conv1x1 proj| SE
+    TD -->|L_RKD: relational| SD
+
+    style Teacher fill:#e3f2fd,stroke:#1565c0
+    style Student fill:#fff3e0,stroke:#e65100
+```
+````
--- a/templates/architecture_nadezhda.md
+++ b/templates/architecture_nadezhda.md
@@ -0,0 +1,115 @@
+# Шаблоны архитектурных диаграмм NADEZHDA
+
+## Mermaid: Полная система Teacher-Student
+
+````markdown
+```mermaid
+graph TB
+    subgraph Training ["ОБУЧЕНИЕ (5 модальностей, облако)"]
+        direction TB
+        SAT_T[🛰 Satellite RGB] --> DINO[DINOv3-L<br/>~300M frozen, patch 16, RegTokens]
+        DRONE_T[🚁 Drone RGB] --> DINO
+        SV[📷 Street-View] --> DINO
+        DEPTH[🗺 Depth Map<br/>DA V2] --> DINO
+        TEXT[📝 Text<br/>MobileCLIP2] --> FILM[Multi-FiLM<br/>γ·F + β]
+        FILM --> DINO
+
+        DINO --> TFEAT[Teacher Features]
+        TFEAT --> TDESC[Teacher Descriptor<br/>512-dim, L2-norm]
+        TFEAT --> TSEG[OV-Seg Head]
+    end
+
+    subgraph Inference ["ИНФЕРЕНС (2 модальности, Jetson)"]
+        direction TB
+        SAT_S[🛰 Satellite RGB] --> FVIT[FastViT-T12<br/>8.5M, weight-shared]
+        DRONE_S[🚁 Drone RGB] --> FVIT
+        FVIT --> SFEAT[Student Features]
+        SFEAT --> CVD[CVD Split]
+        CVD --> SDESC[Student Descriptor<br/>512-dim, L2-norm]
+    end
+
+    TDESC -.->|L_LUPI: MSE| SDESC
+    TFEAT -.->|L_feat: Conv1×1| SFEAT
+    TDESC -.->|L_RKD: relational| SDESC
+    TSEG -.->|L_seg: CE+KL| SFEAT
+
+    style Training fill:#e8eaf6,stroke:#283593
+    style Inference fill:#fff8e1,stroke:#f57f17
+```
+````
+
+## Mermaid: Progressive Loss Staging
+
+````markdown
+```mermaid
+gantt
+    title Progressive Loss Staging (60 epochs)
+    dateFormat X
+    axisFormat %s
+
+    section Losses
+    L_task (InfoNCE)          :active, 0, 60
+    L_feat (alignment)        :crit, 10, 60
+    L_CRD (contrastive repr.) :crit, 10, 60
+    L_LUPI (privileged MSE)   :done, 20, 60
+    L_RKD (relational)        :done, 20, 60
+    L_seg (segmentation)      :done, 20, 60
+    L_CVD (disentanglement)   :done, 20, 60
+
+    section Phases
+    Warmup (L_task only)      :milestone, 0, 10
+    Ramp-up (+L_feat, L_CRD)  :milestone, 10, 20
+    Full (all 7 losses)       :milestone, 20, 60
+```
+````
+
+## Mermaid: Student Backbone Stages
+
+````markdown
+```mermaid
+graph LR
+    IN["Input<br/>[B,3,256,256]"] --> S1["Stage 1<br/>[B,32,96,96]"]
+    S1 --> S2["Stage 2<br/>[B,64,48,48]"]
+    S2 --> BRIDGE["Conv1×1<br/>Bridge"]
+    BRIDGE --> S3["Stage 3<br/>[B,128,24,24]"]
+    S3 --> S4["Stage 4<br/>[B,256,12,12]"]
+    S4 --> POOL["GGeM Pool"]
+    POOL --> DESC["Descriptor<br/>[B,512]"]
+
+    S1 ~~~ NOTE1["SOFIA<br/>(DCN blocks)"]
+    S3 ~~~ NOTE2["MambaVision<br/>(SSM+ViT)"]
+
+    style S1 fill:#fff3e0,stroke:#e65100
+    style S2 fill:#fff3e0,stroke:#e65100
+    style S3 fill:#e3f2fd,stroke:#1565c0
+    style S4 fill:#e3f2fd,stroke:#1565c0
+    style NOTE1 fill:#fff3e0,stroke:none,color:#e65100
+    style NOTE2 fill:#e3f2fd,stroke:none,color:#1565c0
+```
+````
+
+## Mermaid: Data Augmentation Pipeline
+
+````markdown
+```mermaid
+graph LR
+    RAW[Raw RGB<br/>512×512] --> RESIZE[Resize<br/>256×256]
+    RESIZE --> DA["Depth Anything V2<br/>Large (335M)"]
+    RESIZE --> DSINE["DSINE<br/>(30M)"]
+    RESIZE --> SEG["SegFormer-B5<br/>(85M)"]
+
+    DA --> D_OUT["depth.npy<br/>[1,256,256]"]
+    DSINE --> N_OUT["normal.npy<br/>[3,256,256]"]
+    SEG --> S_OUT["seg.npy<br/>[1,256,256]"]
+
+    RESIZE --> RGB["RGB<br/>[3,256,256]"]
+    RGB --> CONCAT["concat_8ch.npy<br/>[8,256,256]"]
+    D_OUT --> CONCAT
+    N_OUT --> CONCAT
+    S_OUT --> CONCAT
+
+    style DA fill:#e8f5e9,stroke:#2e7d32
+    style DSINE fill:#fce4ec,stroke:#c62828
+    style SEG fill:#e3f2fd,stroke:#1565c0
+```
+````