vera-ai-image-reviewing

name	vera-ai-image-reviewing
description	Runs data quality diagnostics and baseline image classification. Produces class distribution tables, sample image grids, image size and channel statistics, a baseline CNN or pre-trained feature extractor with Logistic Regression, weighted F1 and macro AUC (bootstrapped 95% CIs), confusion matrix, and ROC curves. Ends with a recommendation block listing additional models available in the analysis workflow. Outputs Python scripts with 2 publication-quality plots. Triggered when user has image data and says "image classification," "computer vision," "CNN," "object recognition," "medical imaging," "image detection," "visual recognition," "photo classification," "X-ray classification," or describes a task involving classifying images into categories. Does not handle free-text NLP or tabular-only data.
argument-hint	["dataset-path-or-description"]
allowed-tools	Read, Bash, Write, Edit

Image Classification --- Data Diagnostics & Baseline Modeling

Table of Contents

• Scope Boundary
• Workflow
• Decision Tree
• Required Inputs
• Code Structure
• Reporting Standards
• Models Available
• Example Dataset
• Configuration Defaults
• Why These Defaults
• Minimal Smoke Test
• Cross-Skill Interface

Open-source skill.

Scope Boundary

Use this skill when:

• The task is supervised image classification with labeled folders or label metadata.
• A reproducible baseline is needed before transfer learning, ViT, or ensembles.

Do not use this skill when:

• The task is detection, segmentation, captioning, retrieval, video understanding, or generation.
• The dataset is so small that any result should be treated only as a smoke test, not a benchmark.
• The main signal is tabular metadata rather than image pixels.

Workflow

Read each step file in workflow/ before executing that step.

Step	Responsibility	Executor	Document	Input	Output
Collect	Collect Inputs	Main Agent	workflow/step01-collect-inputs.md	User input	Structured input summary
Diagnose	Check Distribution	Main Agent	workflow/step02-check-distribution.md	Prior step output	PART 1 code block
Baseline	Run Primary Test	Main Agent	workflow/step03-run-primary-test.md	Prior step output	PART 2-3 code blocks + T1 track artifacts

Decision Tree

1. CHECK CLASS BALANCE
   ├── Balanced (minority ≥ 10%) → standard methods
   └── Imbalanced (minority < 10%) → weighted loss, data augmentation note

2. IMAGE SIZE STRATEGY
   ├── Uniform size → use directly
   └── Variable size → resize to common dimension (224x224 default)

3. BASELINE MODEL SELECTION
   ├── N ≥ 1000 → Simple CNN (train from scratch)
   └── N < 1000 → Pre-trained feature extractor (ResNet18) + LogReg
   # N=1000 threshold: below this, from-scratch CNNs typically overfit (too few examples
   # per parameter). ResNet18 is deliberately the smallest modern ResNet — large enough
   # to carry useful ImageNet features, small enough that feature-extraction + LogReg is
   # tractable on CPU. Larger backbones (ResNet50, ViT) are reserved for the analyzing skill.

Required Inputs

Role	What to collect
Image source	Directory path, dataset name, or file format
Label source	Subdirectory names, CSV mapping, or metadata
Image format	PNG, JPEG, DICOM, etc.
Number of classes	Binary or multi-class

Code Structure

PART 0: Setup & Data Loading
PART 1: Data Diagnostics          → plot_01_data_overview.png
PART 2: Baseline Classification   → plot_02_confusion_roc.png
PART 3: Recommendation Block      → text pointing to analysis workflow

Reporting Standards

1. Metrics: weighted F1 and macro AUC (OVR) — always with 95% bootstrapped CIs
2. For imbalanced classes (minority < 10%): also report PR-AUC (Average Precision) — primary discrimination metric under imbalance.
3. Format: "F1 = 0.XXX, 95% CI [0.XXX, 0.XXX]"; "PR-AUC = 0.XXX, 95% CI [..., ...]"
4. AUC: "AUC = 0.XXX, 95% CI [0.XXX, 0.XXX]"
5. Decimal places: 3 for F1/AUC, 1 for percentages, 0 for counts
6. Image dimensions: report as HxWxC
7. Sample size: report final analytic N (train/val/test split sizes)
8. Data augmentation: always report what augmentations were applied
9. Bootstrapped CIs: 1000 iterations, 2.5th/97.5th percentiles

Models Available

Status	Models
Implemented in this skill	Simple CNN (larger datasets) or ResNet18 feature extractor + Logistic Regression (small datasets)
Implemented downstream in vera-ai-image-generating	ResNet50, EfficientNet-B0, VGG16 (legacy replication only), DenseNet121, ViT-B/16, soft voting, stacking, GradCAM, attention maps
Planned but not yet shipped	ConvNeXt-Tiny is a roadmap item only; there is no open-source src/ module for it yet

Example Dataset

torchvision built-ins: CIFAR10, FashionMNIST, MNIST. Python: from torchvision.datasets import CIFAR10

Configuration Defaults

Pipeline constants live in config/default.json. Read it before generation. Key knobs:

• alpha, ci_level — inference thresholds
• plot_dpi, plot_width, plot_height — figure dimensions
• bootstrap_iterations — bootstrap CIs for metrics
• split.{test_size, val_size_of_train, random_state} — data-split reproducibility
• image.{resize, normalize_mean, normalize_std} — preprocessing (ImageNet norms by default)
• baseline.{N_threshold, small_N_model, large_N_model} — N-dependent baseline choice (ResNet18 feature extractor for small N; simple CNN for large N)
• augmentation.* — train-time augmentation pipeline
• class_balance_threshold — minority-class rare-event threshold

To override: create config/local.json; the runtime merges local over default.

Why These Defaults

• baseline.N_threshold = 1000 is a practical overfitting guardrail, not a formal cutoff. Below that scale, frozen transfer features usually behave more predictably than a from-scratch CNN in open-source baseline runs.
• image.resize = 224x224 and ImageNet normalization are defaults because they align with the shipped torchvision backbones and make the baseline comparable to the downstream transfer-learning models.
• PR-AUC is promoted under class imbalance for the same reason as the NLP and structured skills: ROC-AUC often looks healthier than the positive-class retrieval story when negatives dominate.

Minimal Smoke Test

• Smoke-test prompt: "Run vera-ai-image-reviewing on a 2-4 class subset of CIFAR-10 or an equivalent ImageFolder dataset. Produce the standard T1 artifacts."
• Suggested fixture: 20-100 images per class for a CPU-only smoke test; use the ResNet18 feature-extractor branch if the sample is small.
• Expected pass condition: the run writes one runnable Python script, methods.md, results.md, tables/, references.bib, and the two baseline figures listed below.

Cross-Skill Interface

Output:
├── code_python      → .py script
├── methods_md       → methods.md baseline fragment
├── results_md       → results.md baseline results fragment
├── tables/          → Markdown/CSV tables for dataset stats + metrics
├── figures/         → 2 PNGs (data overview + confusion/ROC)
├── references_bib   → .bib with baseline/evaluation citations
└── recommendations  → text block (what analysis workflow produces)

When this skill is used as T1_baseline inside vera-ai-application-pipelining, the standardized track artifacts above are REQUIRED, not optional.