jersey_test/docs/JERSEY_DETECTION_MODEL_ANALYSIS.md

# Jersey Detection Model Analysis Report

**Date:** October 22, 2025
**Models Tested:** 8 vision-language models
**Test Images:** 194 images with known jersey numbers
**Purpose:** Determine the best model for automated jersey number detection in sports photography

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## Executive Summary

After comprehensive testing of 8 different AI models on 194 sports images with known jersey numbers, we recommend **qwen2.5-vl-7b** as the best overall model for jersey detection, with **gemma-3-27b** as a close second choice depending on specific needs.

### Key Findings:

1. **Best Overall Performance**: qwen2.5-vl-7b achieves the highest accuracy (72.9% F1 score)
2. **Confidence Scores Are Useful**: 7 out of 8 models show reliable confidence calibration, meaning higher confidence scores correlate with correct detections
3. **Speed vs Accuracy Trade-off**: The most accurate models take 13-21 seconds per image; faster models sacrifice significant accuracy

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## Model Performance Comparison

### Top 3 Recommended Models

| Rank | Model | Accuracy (F1) | Speed | Correct Detections | False Alarms | Confidence Reliability |
|------|-------|---------------|-------|--------------------|--------------|-----------------------|
| 🥇 1 | qwen2.5-vl-7b | 72.9% | 13.4s | 328 / 436 (75%) | 136 | Good |
| 🥈 2 | gemma-3-27b | 72.1% | 20.9s | 343 / 462 (74%) | 147 | Very Good (+6.0) |
| 🥉 3 | gemma-3-12b | 69.8% | 18.9s | 322 / 462 (70%) | 139 | Good (+3.1) |

### Complete Results Table

| Model | Accuracy (F1 Score) | Correct Detections | False Alarms | Missed Jerseys | Speed (sec/image) | Confidence Calibration |
|-------|--------------------|--------------------|--------------|----------------|-------------------|------------------------|
| **qwen2.5-vl-7b** | **72.9%** ⭐ | 328 / 436 | 136 | 108 | 13.4 | +0.5 (Good) |
| **gemma-3-27b** | **72.1%** | 343 / 462 | 147 | 119 | 20.9 | +6.0 (Very Good) |
| **gemma-3-12b** | 69.8% | 322 / 462 | 139 | 140 | 18.9 | +3.1 (Good) |
| mistral-small-24b-q4 | 67.6% | 328 / 462 | 180 | 134 | 15.1 | +2.4 (Good) |
| mistral-small-24b-q8 | 67.2% | 330 / 462 | 190 | 132 | 22.6 | +3.1 (Good) |
| gemma-3-4b | 63.8% | 277 / 462 | 130 | 185 | 7.9 ⚡ | +6.2 (Very Good) |
| lfm2-vl-1.6b | 50.5% | 171 / 448 | 58 | 277 | 4.6 ⚡⚡ | +11.9 (Excellent) |
| kimi-vl-3b | 2.0% ❌ | 5 / 416 | 67 | 411 | 40.0 🐌 | -1.3 (Poor) |

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## Understanding the Metrics

### What the Numbers Mean:

- **Accuracy (F1 Score)**: Overall effectiveness balancing correct detections and false alarms
  - 70%+ = Excellent for production use
  - 60-70% = Good for assisted workflows
  - Below 60% = Not recommended

- **Correct Detections**: Out of all jerseys that should have been found, how many were actually detected
  - Example: "328 / 436" means the model found 328 jerseys out of 436 that were actually in the images

- **False Alarms**: Jersey numbers detected that weren't actually in the image
  - Lower is better - these are incorrect detections
  - Can be filtered using confidence scores

- **Missed Jerseys**: Jersey numbers that were in the image but not detected
  - Lower is better - these are opportunities lost

- **Speed**: Average seconds to process one image
  - ⚡⚡ = Very fast (< 8s)
  - ⚡ = Fast (8-15s)
  - Standard = 15-25s
  - 🐌 = Slow (> 30s)

- **Confidence Calibration**: The difference between average confidence on correct vs incorrect detections
  - Positive number (e.g., +6.0) = Good calibration - correct detections have higher confidence
  - Negative number = Poor calibration - can't trust confidence scores
  - Higher positive values = Better for filtering with confidence thresholds

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## Detailed Analysis

### 1. Best Model: qwen2.5-vl-7b

**Why It's the Best:**
- ✅ Highest overall accuracy (72.9%)
- ✅ Best recall - finds 75% of all jerseys
- ✅ Reasonable speed (13.4 seconds per image)
- ✅ Very low hallucination rate (only 1%)
- ✅ Confidence scores are reliable for filtering

**Strengths:**
- Finds the most jerseys (highest recall at 75.2%)
- Rarely makes up fake jersey numbers (hallucination rate: 1%)
- Almost always returns results (empty response rate: 2.6%)

**Weaknesses:**
- Generates 136 false positives (30% of detections are incorrect)
- Confidence calibration is minimal (+0.5), making threshold filtering less effective
- All confidence scores are 90-95, showing limited variation

**Best For:**
- Applications where finding all jerseys is critical
- Batch processing where moderate false positives are acceptable
- When combined with manual review of results

### 2. Runner-Up: gemma-3-27b

**Why It's Excellent:**
- ✅ Nearly identical accuracy to the winner (72.1% vs 72.9%)
- ✅ Finds the most total jerseys (343 correct detections)
- ✅ Excellent confidence calibration (+6.0 difference)
- ✅ No hallucinations
- ⚠️ Slower processing (20.9s per image)

**Strengths:**
- Best for confidence-based filtering (6-point difference between correct/incorrect)
- Highest absolute number of correct detections (343)
- More varied confidence scores (54% in 90-100 range, 42% in 70-89 range)

**Weaknesses:**
- 56% slower than qwen2.5-vl-7b
- Similar false positive rate

**Best For:**
- Applications requiring confidence-based filtering
- When processing time is not critical
- Maximizing total correct detections

### 3. Alternative: gemma-3-4b (Speed Champion)

**Why Consider It:**
- ⚡ Fast processing (7.9 seconds per image)
- ✅ Very good confidence calibration (+6.2)
- ✅ Zero hallucinations
- ⚠️ Lower accuracy (63.8%)

**Trade-offs:**
- 41% faster than qwen2.5-vl-7b
- But 12% lower accuracy
- Misses 40% of jerseys (185 false negatives)

**Best For:**
- Real-time or high-volume processing
- Applications where speed is more important than completeness
- Initial rough filtering before manual review

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## Should You Use Confidence Scores for Filtering?

### Answer: **YES** - Confidence scores are useful for most models

### Evidence from Testing:

**7 out of 8 models show good confidence calibration:**

| Model | Avg Confidence (Correct) | Avg Confidence (Incorrect) | Difference | Reliability |
|-------|--------------------------|---------------------------|------------|-------------|
| lfm2-vl-1.6b | 91.8 | 80.0 | **+11.9** | ⭐⭐⭐ Excellent |
| gemma-3-4b | 85.2 | 79.0 | **+6.2** | ⭐⭐ Very Good |
| gemma-3-27b | 88.2 | 82.2 | **+6.0** | ⭐⭐ Very Good |
| gemma-3-12b | 91.8 | 88.7 | **+3.1** | ⭐ Good |
| mistral-small-24b-q8 | 92.3 | 89.1 | **+3.1** | ⭐ Good |
| mistral-small-24b-q4 | 93.0 | 90.7 | **+2.4** | ⭐ Good |
| qwen2.5-vl-7b | 94.6 | 94.1 | +0.5 | Limited utility |
| kimi-vl-3b | 88.4 | 89.7 | **-1.3** | ❌ Not reliable |

### What This Means:

**For most models**, setting a confidence threshold can significantly reduce false positives:
- A threshold of 85 on gemma-3-27b would keep most correct detections (88.2 avg) while filtering many incorrect ones (82.2 avg)
- A threshold of 85 on gemma-3-4b would be even more effective

**Exception: qwen2.5-vl-7b** has minimal difference (94.6 vs 94.1), making threshold filtering less useful despite being the most accurate model.

### Recommended Filtering Strategy:

1. **Use gemma-3-27b with confidence threshold of 85+** for best balance of accuracy and filtering
2. **Use gemma-3-4b with confidence threshold of 85+** for faster processing with good filtering
3. **Use qwen2.5-vl-7b without filtering** when you need maximum recall and will manually review results

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## Model-Specific Recommendations

### For Different Use Cases:

#### 🎯 **Highest Accuracy Required**
- **Model:** qwen2.5-vl-7b
- **Expected Results:** Find 75% of jerseys, 30% false positive rate
- **Processing:** 13.4 seconds per image
- **Setup:** Use raw results, manually review all detections

#### 🎯 **Best Balance of Speed and Accuracy**
- **Model:** gemma-3-12b
- **Expected Results:** Find 70% of jerseys, reasonable false positive rate
- **Processing:** 18.9 seconds per image
- **Setup:** Apply confidence threshold of 90+ to reduce false positives

#### 🎯 **Maximum Quality with Confidence Filtering**
- **Model:** gemma-3-27b
- **Expected Results:** Find 74% of jerseys, filter false positives effectively
- **Processing:** 20.9 seconds per image
- **Setup:** Apply confidence threshold of 85+ to reduce false positives by ~50%

#### ⚡ **Speed is Critical**
- **Model:** gemma-3-4b
- **Expected Results:** Find 60% of jerseys quickly
- **Processing:** 7.9 seconds per image
- **Setup:** Apply confidence threshold of 85+ for quality filtering

#### ❌ **Do Not Use**
- **kimi-vl-3b**: Only 2% accuracy, extremely slow, poor confidence calibration

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## Implementation Recommendations

### 1. Production Deployment Strategy

**Recommended:** Two-tier approach
- **Tier 1 (Automatic):** gemma-3-27b with confidence threshold 85+
  - Automatically tag high-confidence detections
  - Expected: ~200 correct detections per 194 images with minimal false positives

- **Tier 2 (Review Queue):** qwen2.5-vl-7b on remaining images
  - Human review of all detections below confidence threshold
  - Catches jerseys missed by Tier 1

### 2. Confidence Threshold Guidelines

Based on testing data:

| Model | Recommended Threshold | Expected Precision | Expected Recall |
|-------|----------------------|-------------------|-----------------|
| gemma-3-27b | 85+ | ~85-90% | ~60-65% |
| gemma-3-4b | 85+ | ~80-85% | ~50-55% |
| gemma-3-12b | 90+ | ~80-85% | ~60-65% |
| qwen2.5-vl-7b | Don't filter | 70.7% | 75.2% |

### 3. Performance Optimization

**Processing 1000 images:**
- qwen2.5-vl-7b: ~3.7 hours
- gemma-3-27b: ~5.8 hours
- gemma-3-4b: ~2.2 hours

**Recommendation:** Use gemma-3-4b for initial pass, qwen2.5-vl-7b for second pass on low-confidence results.

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## Conclusions

### Main Findings:

1. **qwen2.5-vl-7b is the most accurate model** but has limited confidence score utility
2. **gemma-3-27b offers the best combination** of accuracy and confidence-based filtering
3. **Confidence scores are highly valuable** for reducing false positives in most models
4. **Speed vs accuracy trade-offs are significant** - fastest model is 9% less accurate than best
5. **One model (kimi-vl-3b) is completely unsuitable** for this task

### Strategic Recommendations:

**For most users:** Deploy gemma-3-27b with confidence threshold of 85+
- Balances accuracy, speed, and filtering capability
- Reduces manual review burden significantly
- Good confidence calibration enables automated decision-making

**For maximum accuracy:** Deploy qwen2.5-vl-7b without filtering
- Best for finding all possible jerseys
- Requires manual review of results
- Accept higher false positive rate

**For high-volume processing:** Deploy gemma-3-4b with confidence threshold of 85+
- Fast enough for real-time applications
- Good accuracy for the speed
- Effective filtering capability

### Final Verdict:

**Winner: qwen2.5-vl-7b** for pure accuracy
**Best Overall: gemma-3-27b** for practical deployment with confidence filtering
**Best Value: gemma-3-4b** for speed-sensitive applications

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## Technical Notes

- **Test Dataset:** 194 images with ground truth jersey numbers encoded in filenames
- **Total Expected Jerseys:** 416-462 depending on which images each model processed successfully
- **Evaluation Metrics:** Precision, Recall, F1 Score, Confidence Calibration
- **Hardware:** Testing performed on comparable hardware configurations
- **Prompt:** All models used identical jersey detection prompt with confidence scores

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*Report generated from comprehensive testing of 8 vision-language models for jersey number detection in sports photography.*