From b5432c9ef7f92e7525a8305e25570b88e10d118d Mon Sep 17 00:00:00 2001 From: Rick McEwen Date: Wed, 7 Jan 2026 12:44:15 -0500 Subject: [PATCH] Add comprehensive model comparison analysis --- test_results/FINAL_MODEL_ANALYSIS.md | 216 +++++++++++++++++++++++++++ 1 file changed, 216 insertions(+) create mode 100644 test_results/FINAL_MODEL_ANALYSIS.md diff --git a/test_results/FINAL_MODEL_ANALYSIS.md b/test_results/FINAL_MODEL_ANALYSIS.md new file mode 100644 index 0000000..65a5674 --- /dev/null +++ b/test_results/FINAL_MODEL_ANALYSIS.md @@ -0,0 +1,216 @@ +# Logo Recognition Model Analysis + +**Date:** January 7, 2026 +**Purpose:** Determine the best model and threshold for logo recognition of logos not currently in the test set. + +--- + +## Executive Summary + +| Model | Best Threshold | F1 Score | Precision | Recall | Recommended Use | +|-------|---------------|----------|-----------|--------|-----------------| +| **Image-Split Fine-tuned** | 0.70-0.75 | **67-68%** | 66-80% | 59-68% | Known logos (in reference set) | +| Baseline CLIP | 0.70 | 57-60% | 48-49% | 72-77% | Unknown logos (never seen before) | +| Logo-Split Fine-tuned | 0.76 | 56% | 49% | 64% | Not recommended | +| DINOv2 (small/large) | - | 29-30% | 22-32% | 28-43% | Not suitable | + +**Winner: Image-Split Fine-tuned Model** at threshold **0.70-0.75** + +--- + +## Detailed Model Comparison + +### 1. Baseline CLIP (openai/clip-vit-large-patch14) + +The pre-trained CLIP model without any fine-tuning. + +**Threshold Performance:** + +| Threshold | Precision | Recall | F1 | +|-----------|-----------|--------|-----| +| 0.70 | 47.9% | 71.8% | 57.5% | +| 0.80 | 33.0% | 63.1% | 43.4% | +| 0.85 | 26.9% | 43.4% | 33.2% | +| 0.90 | 54.9% | 22.8% | 32.2% | + +**Similarity Distribution:** +- True Positive mean: 0.854 (range: 0.75-0.95) +- False Positive mean: 0.846 (range: 0.75-0.95) +- **Problem:** TP and FP distributions almost completely overlap + +**Suggested optimal threshold:** 0.756 (predicted F1 = 67.1%) + +**Strengths:** +- Good recall at low thresholds +- Works on completely unseen logos +- No training required + +**Weaknesses:** +- Poor separation between correct and incorrect matches +- High false positive rate + +--- + +### 2. Fine-tuned CLIP (Logo-Level Splits) + +Trained with contrastive learning, tested on completely unseen logo brands. + +**Threshold Performance:** + +| Threshold | Precision | Recall | F1 | +|-----------|-----------|--------|-----| +| 0.70 | 25.9% | 67.1% | 37.4% | +| 0.76 | **49.1%** | 64.3% | **55.7%** | +| 0.82 | 75.7% | 41.4% | 53.5% | +| 0.86 | 88.6% | 28.1% | 42.7% | + +**Similarity Distribution:** +- True Positive mean: 0.853 +- False Positive mean: 0.787 (better separation than baseline) +- Missed logos mean: 0.711 (only 43.7% above 0.75) + +**Suggested optimal threshold:** 0.82 (predicted F1 = 71.9%) + +**Strengths:** +- Better TP/FP separation than baseline +- Very high precision at high thresholds (88.6% at t=0.86) + +**Weaknesses:** +- Does not generalize well to unseen logo brands +- Many correct logos score below threshold (56% of missed logos below 0.75) +- Worse than baseline at threshold 0.70 + +--- + +### 3. Fine-tuned CLIP (Image-Level Splits) ⭐ BEST + +Trained with contrastive learning, all logo brands seen during training (different images held out for testing). + +**Threshold Performance:** + +| Threshold | Precision | Recall | F1 | +|-----------|-----------|--------|-----| +| 0.65 | 56.9% | **75.9%** | 65.0% | +| 0.70 | 66.3% | 68.3% | **67.3%** | +| 0.75 | **79.9%** | 59.3% | **68.1%** | +| 0.80 | 83.7% | 52.8% | 64.8% | +| 0.85 | 92.4% | 42.8% | 58.5% | +| 0.90 | 98.9% | 24.7% | 39.5% | + +**Similarity Distribution:** +- True Positive mean: 0.866 (higher than other models) +- False Positive mean: 0.807 +- TP-FP gap: 0.059 (best separation) +- At t=0.75: 92 TP vs only 38 FP (excellent ratio) + +**Suggested optimal threshold:** 0.755 (predicted F1 = 85.6%) + +**Strengths:** +- Best overall F1 score (68.1% at t=0.75) +- Best precision at any threshold (79.9-98.9%) +- Excellent TP/FP ratio +- Highest true positive similarity scores + +**Weaknesses:** +- Requires logos to be in the reference set during training +- May not generalize to completely novel logos + +--- + +### 4. DINOv2 Models + +Tested for comparison but significantly underperformed. + +| Model | Precision | Recall | F1 | +|-------|-----------|--------|-----| +| DINOv2-small | 22.4% | 42.8% | 29.5% | +| DINOv2-large | 32.2% | 28.5% | 30.2% | + +**Not recommended** for logo recognition tasks. + +--- + +## Recommendations + +### For Logo Recognition of Known Logos (logos in your reference set) + +**Use: Image-Split Fine-tuned Model** + +```bash +# Recommended configuration +python test_logo_detection.py \ + -e models/logo_detection/clip_finetuned_image_split \ + -t 0.70 \ + --matching-method multi-ref \ + --use-max-similarity +``` + +| Use Case | Threshold | Expected Performance | +|----------|-----------|---------------------| +| Balanced (recommended) | 0.70 | 66% precision, 68% recall, 67% F1 | +| High precision | 0.75 | 80% precision, 59% recall, 68% F1 | +| Very high precision | 0.80 | 84% precision, 53% recall, 65% F1 | +| Maximum precision | 0.85+ | 92%+ precision, <43% recall | + +### For Logo Recognition of Unknown Logos (completely novel brands) + +**Use: Baseline CLIP** (the fine-tuned models don't generalize well) + +```bash +# Recommended configuration +python test_logo_detection.py \ + -e openai/clip-vit-large-patch14 \ + -t 0.70 \ + --matching-method multi-ref \ + --use-max-similarity +``` + +Expected: ~48% precision, ~72% recall, ~58% F1 + +--- + +## Key Findings + +### 1. Image-Level Splits Dramatically Improve Performance + +The image-split fine-tuned model outperforms all others because: +- It learns brand-specific features during training +- Test images are different but from same brands +- Better represents real-world use where you have reference images for logos you want to detect + +### 2. Logo-Level Splits Test True Generalization (but results are poor) + +The logo-split model tests whether fine-tuning helps with completely unseen logos: +- Result: It doesn't help much (56% F1 vs 58% baseline) +- Contrastive learning doesn't transfer well to novel brands +- Use baseline CLIP for novel logo detection + +### 3. Threshold Sweet Spot is 0.70-0.75 + +For all models, the optimal F1 occurs around threshold 0.70-0.75: +- Lower thresholds: Too many false positives +- Higher thresholds: Misses too many correct logos +- At 0.90+: Precision is high but recall drops below 25% + +### 4. Precision-Recall Tradeoff + +| Priority | Threshold | Tradeoff | +|----------|-----------|----------| +| Recall | 0.65-0.70 | More matches, more false positives | +| Balanced | 0.70-0.75 | Best F1 score | +| Precision | 0.75-0.80 | Fewer false positives, misses some matches | +| High Precision | 0.85+ | Very few false positives, misses many matches | + +--- + +## Conclusion + +**For production use with known logos:** +- Use **Image-Split Fine-tuned Model** at **threshold 0.70-0.75** +- Expected F1: 67-68%, Precision: 66-80% + +**For discovering unknown logos:** +- Use **Baseline CLIP** at **threshold 0.70** +- Expected F1: ~58%, Precision: ~48% + +The image-split fine-tuning provides significant improvements (+8-10% F1) over baseline for known logos, but does not help with completely novel brands. For a production system, ensure all target logos are included in the training/reference set.