vera-ai-image-generating

name	vera-ai-image-generating
description	Server-side extension that completes the full analysis pipeline for image classification after vera-ai-image-reviewing has run. Adds ResNet50, EfficientNet-B0, VGG16, DenseNet121 CNN classifiers with transfer learning (feature extraction and full fine-tuning), subgroup analysis by metadata or image properties, advanced models (ViT transformer with optional tabular fusion and hyperparameter search), ensemble methods, cross-method comparison with unified feature attribution (GradCAM, attention maps) on a 0-100 scale, and manuscript-ready methods.md and results.md. Applies output variation and code style diversity for natural, non-repetitive output. Open-source skill. Triggered after vera-ai-image-reviewing completes and its PART 0–2 artifacts are present (see ../../CROSS-SKILL-INTERFACE.md). If invoked directly without those artifacts, halts and prompts the user to run testing first or supply equivalent PART 0–2 code.
argument-hint	["testing-output-dir-or-dataset"]
allowed-tools	Read, Bash, Write, Edit, Grep, Glob

Image Classification --- Full Analysis & Manuscript Generation

Table of Contents

• Scope Boundary
• Workflow
• Additional Inputs
• Output Structure
• Key References (read before generation)
• Reporting Standards
• Method Status
• Configuration Defaults
• Why These Defaults
• GPU Availability
• Minimal Smoke Test
• Cross-Skill Interface

Open-source skill. Read reference/specs/output-variation-protocol.md before every generation --- apply all variation layers for natural, diverse output.

Scope Boundary

Use this skill when:

• vera-ai-image-reviewing has already produced the baseline artifacts.
• The task is supervised image classification and the user wants transfer learning, ViT, ensembles, and interpretability.

Do not use this skill when:

• The task is detection, segmentation, captioning, retrieval, generation, or video.
• The user expects every contemporary vision backbone; this open-source build only guarantees the methods listed below.
• Hardware limits make anything beyond feature-extraction smoke tests unrealistic.

Workflow

Continues from where vera-ai-image-reviewing stopped (PART 0-2 done).

Step	Responsibility	Executor	Document	Input	Output
Additional CNN models	Run Additional Models	Main Agent	workflow/step04-run-additional-models.md	Prior step output	PART 3 code + prose
Subgroup	Analyze Subgroups	Main Agent	workflow/step05-analyze-subgroups.md	Prior step output	PART 4 code + prose
Advanced models	Fit Advanced Models	Main Agent	workflow/step06-fit-advanced-models.md	Prior step output	PART 5 code + prose
Comparison	Compare Models	Main Agent	workflow/step07-compare-models.md	Prior step output	PART 6 code + prose
Manuscript	Generate Manuscript	Main Agent	workflow/step08-generate-manuscript.md	Prior step output	methods.md + results.md

Additional Inputs

Collect if not already provided:

• Target discipline (for reporting conventions)
• Target journal or style (CVPR, ECCV, MICCAI, NeurIPS, etc.)
• Research question / hypothesis
• Subgroup variable or image property for stratification
• Image dimensions (HxWxC) and any DICOM metadata if medical imaging

Output Structure

output/
├── methods.md
├── results.md
├── tables/             <- Markdown + CSV per table
├── figures/            <- PNGs, 300 DPI
├── references.bib
└── code.py             <- Style-varied

Key References (read before generation)

File	Purpose
reference/specs/output-variation-protocol.md	Output quality variation layers
reference/specs/code-style-variation.md	Seven-dimension code style diversity
reference/patterns/sentence-bank.md	4-6 phrasings per result type
reference/rules/reporting-standards.md	Hard rules for CNN/ViT reporting

Reporting Standards

Same as vera-ai-image-reviewing, plus:

• All models: report F1 (weighted) and AUC (macro) with bootstrapped 95% CIs
• Transfer learning: report backbone name, pre-trained weights, frozen vs fine-tuned layers
• Training: report epochs, best epoch, learning rate, batch size, data augmentation pipeline
• ViT: report pre-trained model name, patch size, whether backbone was frozen
• Image input: report HxWxC dimensions, normalization (mean/std), parameter counts, FLOPs
• Feature attribution: unified 0-100 scale via GradCAM (CNNs) and attention maps (ViT)
• Model comparison: frame as convergent findings, not horse race
• Small dataset CNN: frame as "exploratory"; never claim generalizability
• Medical imaging: report DICOM preprocessing, windowing, DenseNet rationale

Method Status

Status	Methods
Implemented in this skill	ResNet50, EfficientNet-B0, VGG16, DenseNet121, ViT-B/16, soft voting, stacking, GradCAM, ViT attention maps
Legacy-only support	VGG16 is kept for replication and sanity-check comparisons, not as a recommended modern default
Planned but not yet shipped	ConvNeXt-Tiny is not part of the open-source engine yet

Configuration Defaults

Pipeline constants live in config/default.json. Read it before generation. Key knobs in addition to the testing-skill config:

• transfer.{backbones, frozen_below_N, fine_tune_above_N} — N-dependent feature-extraction vs fine-tuning decision
• backbones.* — ResNet50, EfficientNet-B0, VGG16, DenseNet121, ViT hyperparameters
• training.{epochs, batch_size, learning_rate, optimizer, scheduler} — defaults per backbone
• augmentation.{train_pipeline, test_pipeline} — augmentation recipe
• gpu.{required, fallback_to_cpu} — CPU fallback behavior (see GPU Availability below)
• interpretability.{gradcam_layer, vit_attention} — explanation method per model class

To override: create config/local.json.

Why These Defaults

• The transfer-learning split between feature extraction and full fine-tuning exists to keep small-data runs from overclaiming what the labels can support.
• ViT is intentionally narrowed to ViT-B/16 because that is the variant implemented in src/dl_vit.py; the skill should not imply broader transformer coverage than the code actually ships.
• Small-dataset CNN results stay marked "exploratory" because backbone capacity can easily outrun the evidence available in limited image collections.

GPU Availability

Check at workflow step 06 (advanced models) start. Implementation pattern:

import torch
GPU_AVAILABLE = torch.cuda.is_available()
if not GPU_AVAILABLE:
    print("WARNING: CUDA not available. Fine-tuning large backbones on CPU is impractical.")
    print("Falling back to ResNet18/ResNet50 feature-extraction mode only (frozen backbones + LogReg head).")
    TRAINING_MODE = "feature_extraction_only"
else:
    TRAINING_MODE = "full"

Always log the decision to RESEARCH_LOG.md so the manuscript methods section can note training environment.

Minimal Smoke Test

• Smoke-test prompt: "Continue from vera-ai-image-reviewing on a 2-4 class CIFAR-10 or ImageFolder subset. Produce the transfer-learning, advanced-model, and manuscript-ready artifacts."
• CPU pass condition: the skill logs that large-model fine-tuning is impractical and falls back to feature-extraction mode. GPU-heavy steps may be reduced, but the artifact contract must still be satisfied.
• Expected artifacts: methods.md, results.md, tables/, figures/, references.bib, and one consolidated code.py.

Cross-Skill Interface

Method Unit Contract:
├── code_python      -> .py script (style-varied)
├── methods_md       -> methods.md (varied structure)
├── results_md       -> results.md (varied phrasing)
├── tables/          -> Markdown + CSV
├── figures/         -> PNGs 300 DPI (varied layout)
├── references_bib   -> .bib with cited references
└── comparison       -> cross-method narrative (in results.md)