Review Dataset

Review Dataset

Dataset quality audit + profiling for architecture selection.

Workflow

1. Load Config

Read yolo-project.yaml for dataset path, class names, and imgsz. If no config, ask user for dataset path.

2. Run Structural Validation

yolo-validate  --strict

3. Deep Analysis

Scan label files for:

• Class Distribution: Count per class, imbalance ratio. CRITICAL if any class 0.9)
• Empty images: Count and percentage
• Split Balance: Compare class ratios between train and val

3.5 Visual Spot-Check (Claude Vision)

Sample random images and visually verify annotation quality using Claude's multimodal vision.

Categorize images by annotation type:

• Both classes: images containing all project classes
• Single-class only: images with only one class (e.g., body without head)
• Empty: negative samples with no annotations

For each category, sample 2-4 images:

1. Draw annotations using python scripts/draw_annotations.py
2. Read the annotated image with Claude vision
3. Assess:

• Are visible objects properly annotated?
• Are there missing annotations (visible objects with no box)?
• Are boxes accurately placed?
• Are class labels correct?

Flag issues:

• Incomplete annotations: If a significant % of single-class images have visible objects of the missing class (e.g., body annotated but visible head not annotated), flag as "incomplete annotations" with estimated scope
• Misclassified: Wrong class assigned to a box
• Phantom boxes: Boxes on non-objects (UI elements, background)

Report findings:

Include visual spot-check results in the audit report with:

• Number of images sampled per category
• Issues found with example filenames
• Estimated % of dataset affected
• Recommendation: auto-label, manual review, or acceptable as-is

4. Dataset Profile for Architecture Selection

Run the profiling script with imgsz from yolo-project.yaml (default 640):

python scripts/profile_dataset.py \
  --labels /labels/train \
  --images /images/train \
  --imgsz  \
  --class-names ""

The script outputs structured YAML with:

• Object scale distribution (% small/medium/large at training resolution)
• Min object size (px) at training resolution
• Class-wise profiles sorted by avg object size ascending (smallest first)
• Train/val scale divergence check (flags >15% difference in small_pct)
• Suggested starting point for architecture (head config + scale + reasoning)

Save the output to experiments/dataset_profile.yaml.

5. Write to training-plan.md

Fill the Dataset Summary section in training-plan.md with profile data:

• Total images: train/val counts
• Classes: N — [list]
• Class balance: most/least represented with counts
• Scale distribution: % small/medium/large at imgsz
• Min object size at training resolution
• Avg objects per image
• Train/val divergence flag

6. Write Report

Create experiments/dataset_audit.md with:

• Validation findings
• Class distribution table
• Quality issues
• Dataset profile (full YAML output)
• Architecture suggestion (labeled "Suggested starting point", NOT "Recommended")
• Top 3 recommendations

7. Print Summary

Key findings, profile highlights, and architecture suggestion.

Guidelines

• Be specific: "Collect 400+ bird images" not "collect more data"
• Profile uses training-resolution-adjusted sizes, not native pixel sizes
• Architecture suggestion provides data for agent reasoning — it's not a directive

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: MacroMan5
• Source: MacroMan5/autotrain-yolo
• License: MIT

Install and usage instructions live in the source repository linked above.