docs/PHASE1D_EVALUATION_GUIDE.md

CompI Phase 1.D: Baseline Output Quality Evaluation Guide

🎯 Overview

Phase 1.D provides comprehensive tools for systematically evaluating the quality, coherence, and consistency of images generated by your CompI pipeline. This phase is crucial for understanding your model's performance and tracking improvements over time.

🛠️ Tools Provided

1. Streamlit Web Interface (compi_phase1d_evaluate_quality.py)

• Interactive visual evaluation with side-by-side image and metadata display
• Multi-criteria scoring system (1-5 stars) for comprehensive assessment
• Objective metrics calculation (perceptual hashes, file size, dimensions)
• Persistent evaluation logging with CSV export
• Batch evaluation capabilities for efficient processing

2. Command-Line Interface (compi_phase1d_cli_evaluation.py)

• Batch processing for automated evaluation workflows
• Statistical analysis and performance summaries
• Detailed report generation with recommendations
• Filtering and listing capabilities for organized review

3. Convenient Launcher (run_evaluation.py)

• One-click startup for the web interface
• Automatic environment checking and error handling

📊 Evaluation Criteria

The evaluation system uses 5 comprehensive criteria, each scored on a 1-5 scale:

1. Prompt Adherence

• How well does the image match the text prompt?
• Scale: 1=Poor match → 5=Perfect match

2. Style Consistency

• How well does the image reflect the intended artistic style?
• Scale: 1=Style not evident → 5=Style perfectly executed

3. Mood & Atmosphere

• How well does the image convey the intended mood/atmosphere?
• Scale: 1=Wrong mood → 5=Perfect mood

4. Technical Quality

• Overall image quality (resolution, composition, artifacts)
• Scale: 1=Poor quality → 5=Excellent quality

5. Creative Appeal

• Subjective aesthetic and creative value
• Scale: 1=Unappealing → 5=Highly appealing

🚀 Quick Start

Web Interface (Recommended for Manual Review)

# Install required dependency
pip install imagehash

# Launch the evaluation interface
python run_evaluation.py

# Or run directly
streamlit run src/generators/compi_phase1d_evaluate_quality.py

The web interface will open at http://localhost:8501 with:

• Single Image Review: Detailed evaluation of individual images
• Batch Evaluation: Quick scoring for multiple images
• Summary Analysis: Statistics and performance insights

Command-Line Interface (For Automation)

# Analyze existing evaluations
python src/generators/compi_phase1d_cli_evaluation.py --analyze

# List unevaluated images
python src/generators/compi_phase1d_cli_evaluation.py --list-unevaluated

# Batch score all unevaluated images (prompt, style, mood, quality, appeal)
python src/generators/compi_phase1d_cli_evaluation.py --batch-score 4 3 4 4 3 --notes "Initial baseline evaluation"

# Generate detailed report
python src/generators/compi_phase1d_cli_evaluation.py --report --output evaluation_report.txt

📁 File Structure

outputs/
├── [generated images].png          # Your CompI-generated images
├── evaluation_log.csv              # Detailed evaluation data
└── evaluation_summary.json         # Summary statistics

src/generators/
├── compi_phase1d_evaluate_quality.py    # Main Streamlit interface
└── compi_phase1d_cli_evaluation.py      # Command-line tools

run_evaluation.py                    # Convenient launcher

📈 Understanding Your Data

Evaluation Log (outputs/evaluation_log.csv)

Contains detailed records with columns:

• Image metadata: filename, prompt, style, mood, seed, variation
• Evaluation scores: All 5 criteria scores (1-5)
• Objective metrics: dimensions, file size, perceptual hashes
• Evaluation metadata: timestamp, notes, evaluator comments

Key Metrics to Track

1. Overall Score Trends: Are your images improving over time?
2. Criteria Performance: Which aspects (prompt match, style, etc.) need work?
3. Style/Mood Effectiveness: Which combinations work best?
4. Consistency: Are similar prompts producing consistent results?

🎯 Best Practices

Systematic Evaluation Workflow

1. Generate a batch of images using your CompI tools
2. Evaluate systematically using consistent criteria
3. Analyze patterns in the data to identify strengths/weaknesses
4. Adjust generation parameters based on insights
5. Re-evaluate to measure improvements

Evaluation Tips

• Be consistent in your scoring criteria across sessions
• Use notes to capture specific observations and issues
• Evaluate in batches of similar style/mood for better comparison
• Track changes over time as you refine your generation process

Interpreting Scores

• 4.0+ average: Excellent performance, ready for production use
• 3.0-3.9 average: Good performance, minor improvements needed
• 2.0-2.9 average: Moderate performance, significant improvements needed
• Below 2.0: Poor performance, major adjustments required

🔧 Advanced Usage

Filtering and Analysis

# Analyze only specific styles
python src/generators/compi_phase1d_cli_evaluation.py --analyze --style "anime"

# List images by mood
python src/generators/compi_phase1d_cli_evaluation.py --list-all --mood "dramatic"

# Generate style-specific report
python src/generators/compi_phase1d_cli_evaluation.py --report --style "oil painting" --output oil_painting_analysis.txt

Custom Evaluation Workflows

The evaluation tools are designed to be flexible:

• Modify criteria by editing EVALUATION_CRITERIA in the source
• Add custom metrics by extending the get_image_metrics() function
• Integrate with other tools using the CSV export functionality

📊 Sample Analysis Output

📊 CompI Phase 1.D - Evaluation Analysis
==================================================
Total Evaluated Images: 25

📈 Score Statistics:
  Prompt Adherence    : 3.84 ± 0.75 (range: 2-5)
  Style Consistency   : 3.52 ± 0.87 (range: 2-5)
  Mood & Atmosphere   : 3.68 ± 0.69 (range: 2-5)
  Technical Quality   : 4.12 ± 0.60 (range: 3-5)
  Creative Appeal     : 3.76 ± 0.83 (range: 2-5)

🎨 Top Performing Styles (by Prompt Match):
  anime          : 4.20
  oil painting   : 3.90
  digital art    : 3.75

🚀 Next Steps

After completing Phase 1.D evaluation:

1. Identify improvement areas from your evaluation data
2. Experiment with parameter adjustments for low-scoring criteria
3. Document successful combinations for future use
4. Consider Phase 2 development based on baseline performance
5. Set up regular evaluation cycles for continuous improvement

🤝 Integration with Other Phases

Phase 1.D evaluation data can inform:

• Phase 1.A/1.B parameter tuning: Adjust generation settings based on quality scores
• Phase 1.C UI improvements: Highlight best-performing style/mood combinations
• Future phases: Use baseline metrics to measure advanced feature improvements

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Happy Evaluating! 🎨📊

The systematic evaluation provided by Phase 1.D is essential for understanding and improving your CompI system's performance. Use these tools regularly to maintain high-quality output and track your progress over time.