Combine all test results in a single directory

test_results/comparison_results/comparison_summary_20260105_100740.txt

@@ -0,0 +1,29 @@
+============================================================
+
+Test Parameters:
+  Logos: 50, Seed: 42, Threshold: 0.7
+  Method: multi-ref, Refs/logo: 3, Margin: 0.05
+
+BASELINE (openai/clip-vit-large-patch14):
+  True Positives (correct matches):  101
+  False Positives (wrong matches):   104
+  False Negatives (missed logos):    156
+  Precision: 0.4927 (49.3%)
+  Recall:    0.4056 (40.6%)
+  F1 Score:  0.4449 (44.5%)
+
+FINE-TUNED (models/logo_detection/clip_finetuned):
+  True Positives (correct matches):  164
+  False Positives (wrong matches):   414
+  False Negatives (missed logos):    115
+  Precision: 0.2837 (28.4%)
+  Recall:    0.6586 (65.9%)
+  F1 Score:  0.3966 (39.7%)
+
+------------------------------------------------------------
+F1 SCORE COMPARISON:
+  Baseline:    44.5%
+  Fine-tuned:  39.7%
+------------------------------------------------------------
+
+Full results saved to: comparison_results/

test_results/comparison_results_clip_defaults_all_methods.txt

@@ -0,0 +1,124 @@
+Logo Detection Comparison Tests
+================================
+Date: Wed Dec 31 03:43:45 PM MST 2025
+
+Common Parameters:
+  Reference logos: 20
+  Refs per logo: 10
+  Positive samples: 20
+  Negative samples: 100
+  Min matching refs: 3
+  Seed: 42
+
+======================================================================
+TEST: SIMPLE MATCHING
+Method: Simple (all matches above threshold)
+======================================================================
+Date: 2025-12-31 16:02:25
+
+Configuration:
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2355
+  CLIP threshold:            0.7
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      751
+  False Positives:   58221
+  False Negatives:       9
+  Total Expected:      369
+
+Scores:
+  Precision:  0.0127 (1.3%)
+  Recall:     2.0352 (203.5%)
+  F1 Score:   0.0253 (2.5%)
+
+======================================================================
+TEST: MARGIN MATCHING
+Method: Margin-based (margin=0.05)
+======================================================================
+Date: 2025-12-31 16:20:42
+
+Configuration:
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2361
+  CLIP threshold:            0.7
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:       60
+  False Positives:      26
+  False Negatives:     310
+  Total Expected:      369
+
+Scores:
+  Precision:  0.6977 (69.8%)
+  Recall:     0.1626 (16.3%)
+  F1 Score:   0.2637 (26.4%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Method: Multi-ref (mean, min_refs=3, margin=0.05)
+======================================================================
+Date: 2025-12-31 16:38:59
+
+Configuration:
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2352
+  CLIP threshold:            0.7
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      233
+  False Positives:     217
+  False Negatives:     170
+  Total Expected:      369
+
+Scores:
+  Precision:  0.5178 (51.8%)
+  Recall:     0.6314 (63.1%)
+  F1 Score:   0.5690 (56.9%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Method: Multi-ref (max, min_refs=3, margin=0.05)
+======================================================================
+Date: 2025-12-31 16:56:49
+
+Configuration:
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2350
+  CLIP threshold:            0.7
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      278
+  False Positives:     259
+  False Negatives:     136
+  Total Expected:      369
+
+Scores:
+  Precision:  0.5177 (51.8%)
+  Recall:     0.7534 (75.3%)
+  F1 Score:   0.6137 (61.4%)
+

test_results/model_comparison_results.txt

@@ -0,0 +1,105 @@
+Embedding Model Comparison Tests
+=================================
+Date: Fri Jan  2 12:47:03 PM MST 2026
+
+Common Parameters:
+  Matching method: multi-ref (max)
+  Reference logos: 20
+  Refs per logo: 10
+  Positive samples: 20
+  Negative samples: 100
+  Min matching refs: 3
+  Threshold: 0.70
+  Margin: 0.05
+  Seed: 42
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: openai/clip-vit-large-patch14
+Method: Multi-ref (max, min_refs=3, margin=0.05)
+======================================================================
+Date: 2026-01-02 13:05:17
+
+Configuration:
+  Embedding model:           openai/clip-vit-large-patch14
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2355
+  Similarity threshold:      0.7
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      284
+  False Positives:     295
+  False Negatives:     124
+  Total Expected:      369
+
+Scores:
+  Precision:  0.4905 (49.1%)
+  Recall:     0.7696 (77.0%)
+  F1 Score:   0.5992 (59.9%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: facebook/dinov2-small
+Method: Multi-ref (max, min_refs=3, margin=0.05)
+======================================================================
+Date: 2026-01-02 13:19:01
+
+Configuration:
+  Embedding model:           facebook/dinov2-small
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2358
+  Similarity threshold:      0.7
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      158
+  False Positives:     546
+  False Negatives:     234
+  Total Expected:      369
+
+Scores:
+  Precision:  0.2244 (22.4%)
+  Recall:     0.4282 (42.8%)
+  F1 Score:   0.2945 (29.5%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: facebook/dinov2-large
+Method: Multi-ref (max, min_refs=3, margin=0.05)
+======================================================================
+Date: 2026-01-02 13:39:33
+
+Configuration:
+  Embedding model:           facebook/dinov2-large
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2355
+  Similarity threshold:      0.7
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      105
+  False Positives:     221
+  False Negatives:     277
+  Total Expected:      369
+
+Scores:
+  Precision:  0.3221 (32.2%)
+  Recall:     0.2846 (28.5%)
+  F1 Score:   0.3022 (30.2%)
+

test_results/test_results_analysis.md

@@ -346,6 +346,131 @@ DINOv2 Small produces over 3x as many false positives as true positives, making
 
 ---
 
+## Summary and Recommendations
+
+This section synthesizes findings from all test runs to provide actionable recommendations for logo detection configuration and future improvements.
+
+### Best Configuration
+
+Based on all tests conducted, the optimal configuration is:
+
+| Parameter | Recommended Value | Rationale |
+|-----------|-------------------|-----------|
+| **Embedding Model** | `openai/clip-vit-large-patch14` | 2x better F1 than DINOv2 alternatives |
+| **Matching Method** | `multi-ref` with max similarity | Best F1 (59.9%) and recall (77.0%) |
+| **Similarity Threshold** | 0.70 | Lower thresholds outperform higher ones |
+| **Margin** | 0.05 | Minimal impact; keep low to avoid rejecting valid matches |
+| **Min Matching Refs** | 3 | Provides better discrimination than threshold alone |
+| **Refs Per Logo** | 10 | More references improve robustness |
+| **DETR Threshold** | 0.50 | Standard detection confidence |
+
+### Performance Expectations
+
+With the recommended configuration:
+
+| Metric | Expected Value | Interpretation |
+|--------|----------------|----------------|
+| Precision | ~49% | About half of detections are correct |
+| Recall | ~77% | Finds most logos present in images |
+| F1 Score | ~60% | Moderate overall accuracy |
+| FP:TP Ratio | ~1:1 | Approximately equal true and false positives |
+
+**Important**: These results indicate the system is suitable for applications that can tolerate a high false positive rate, such as:
+- Initial screening with human review
+- Flagging content for further analysis
+- Low-stakes logo presence detection
+
+The system is **not suitable** for high-precision applications without additional filtering or verification steps.
+
+### Key Insights from Testing
+
+#### What Works
+
+1. **Multi-ref matching with max aggregation** consistently outperforms other methods
+2. **Multiple references per logo** (10) provides robustness against logo variations
+3. **min_matching_refs=3** is more effective at discrimination than threshold tuning
+4. **CLIP embeddings** significantly outperform self-supervised alternatives (DINOv2)
+
+#### What Doesn't Work
+
+1. **Raising similarity threshold** paradoxically increases false positives in the 0.70-0.85 range
+2. **Margin-only matching** fails with multiple references (same-logo refs compete)
+3. **DINOv2 models** produce 2-3x worse results than CLIP
+4. **Simple threshold-based matching** produces unacceptable 78:1 FP:TP ratio
+
+#### Limitations
+
+1. **~50% precision ceiling**: Even the best configuration produces nearly as many false positives as true positives
+2. **No clean threshold separation**: CLIP's embedding space doesn't provide clear decision boundaries for logos
+3. **General-purpose models**: Neither CLIP nor DINOv2 are optimized for fine-grained logo discrimination
+4. **Pipeline dependencies**: Results depend heavily on DETR detection quality
+
+### Recommendations for Future Improvements
+
+#### Short-Term Improvements
+
+| Improvement | Expected Impact | Effort |
+|-------------|-----------------|--------|
+| **Post-processing filters** | Reduce FP by 20-30% | Low |
+| Add color histogram matching | Filter matches with wrong colors | |
+| Add aspect ratio validation | Reject shape mismatches | |
+| Add text detection | Filter if expected text is missing | |
+| **Reference curation** | Improve TP by 10-20% | Low |
+| Remove low-quality references | Reduce noise in ref embeddings | |
+| Ensure diverse logo variants | Improve coverage | |
+| **Ensemble scoring** | Improve F1 by 10-15% | Medium |
+| Combine CLIP + color features | Multi-signal confidence | |
+| Weighted voting across refs | More robust aggregation | |
+
+#### Medium-Term Improvements
+
+| Improvement | Expected Impact | Effort |
+|-------------|-----------------|--------|
+| **Fine-tune CLIP on logos** | Improve F1 by 20-40% | Medium |
+| Contrastive training on logo pairs | Better embedding separation | |
+| Use LogoDet-3K for training data | Domain-specific features | |
+| **Alternative detection models** | Improve detection quality | Medium |
+| Test YOLOv8 for logo detection | Faster, potentially more accurate | |
+| Train custom detector on logo data | Better region proposals | |
+| **Learned similarity metric** | Improve precision by 30-50% | Medium |
+| Train siamese network for logo matching | Replace cosine similarity | |
+| Learn logo-specific distance function | Better discrimination | |
+
+#### Long-Term Improvements
+
+| Improvement | Expected Impact | Effort |
+|-------------|-----------------|--------|
+| **End-to-end logo recognition model** | F1 > 85% | High |
+| Single model for detection + recognition | Eliminate pipeline errors | |
+| Train on large-scale logo dataset | Comprehensive coverage | |
+| **Logo-specific foundation model** | F1 > 90% | High |
+| Pre-train on millions of logo images | Domain expertise | |
+| Fine-tune for specific brand sets | Production-ready accuracy | |
+
+### Decision Framework
+
+Use this framework to choose between precision and recall:
+
+| Use Case | Priority | Recommended Adjustments |
+|----------|----------|------------------------|
+| **Content moderation** | High recall | Use defaults; accept FPs for human review |
+| **Brand monitoring** | Balanced | Use defaults; filter obvious FPs |
+| **Automated licensing** | High precision | Use threshold=0.90; accept low recall |
+| **Search/discovery** | High recall | Lower threshold to 0.65; more refs |
+
+### Conclusion
+
+The current DETR + CLIP pipeline with multi-ref matching achieves moderate accuracy (~60% F1) that is suitable for screening applications but falls short of production requirements for automated decision-making. The fundamental limitation is that general-purpose vision models lack the fine-grained discrimination needed for logo recognition.
+
+**To achieve production-quality accuracy (>85% F1), the system requires:**
+1. A logo-specific embedding model (fine-tuned or trained from scratch)
+2. Additional visual features beyond CLIP embeddings
+3. Potentially an end-to-end architecture designed for logo recognition
+
+The test framework established here provides the foundation for evaluating these future improvements systematically.
+
+---
+
 ## Test Run: [Next Test Name]
 
 *Results pending...*

test_results/threshold_analysis/finetuned_thresholds_20260105_122213.txt

@@ -0,0 +1,20 @@
+============================================================
+THRESHOLD OPTIMIZATION RESULTS
+Model: finetuned (models/logo_detection/clip_finetuned)
+============================================================
+
+Threshold        TP       FP       FN     Prec   Recall       F1
+--------------------------------------------------------------------
+0.70            167      477      120    25.9%    67.1%    37.4%
+0.72            158      339      116    31.8%    63.5%    42.4%
+0.74            150      252      123    37.3%    60.2%    46.1%
+0.76            160      166      119    49.1%    64.3%    55.7%
+0.78            120      102      147    54.1%    48.2%    51.0%
+0.80            110       73      151    60.1%    44.2%    50.9%
+0.82            103       33      159    75.7%    41.4%    53.5%
+0.84             74       18      180    80.4%    29.7%    43.4%
+0.86             70        9      187    88.6%    28.1%    42.7%
+--------------------------------------------------------------------
+
+BEST THRESHOLD: 0.76 (F1 = 55.7%)
+

test_results/threshold_analysis/threshold_test_results.txt

@@ -0,0 +1,193 @@
+Threshold Optimization Tests
+=============================
+Date: Fri Jan  2 10:11:34 AM MST 2026
+
+Common Parameters:
+  Matching method: multi-ref (max)
+  Reference logos: 20
+  Refs per logo: 10
+  Positive samples: 20
+  Negative samples: 100
+  Min matching refs: 3
+  Seed: 42
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: openai/clip-vit-large-patch14
+Method: Multi-ref (max, min_refs=3, margin=0.05)
+======================================================================
+Date: 2026-01-02 10:29:26
+
+Configuration:
+  Embedding model:           openai/clip-vit-large-patch14
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2358
+  Similarity threshold:      0.7
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      265
+  False Positives:     288
+  False Negatives:     141
+  Total Expected:      369
+
+Scores:
+  Precision:  0.4792 (47.9%)
+  Recall:     0.7182 (71.8%)
+  F1 Score:   0.5748 (57.5%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: openai/clip-vit-large-patch14
+Method: Multi-ref (max, min_refs=3, margin=0.05)
+======================================================================
+Date: 2026-01-02 10:47:35
+
+Configuration:
+  Embedding model:           openai/clip-vit-large-patch14
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2348
+  Similarity threshold:      0.8
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      233
+  False Positives:     472
+  False Negatives:     165
+  Total Expected:      369
+
+Scores:
+  Precision:  0.3305 (33.0%)
+  Recall:     0.6314 (63.1%)
+  F1 Score:   0.4339 (43.4%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: openai/clip-vit-large-patch14
+Method: Multi-ref (max, min_refs=3, margin=0.1)
+======================================================================
+Date: 2026-01-02 11:05:34
+
+Configuration:
+  Embedding model:           openai/clip-vit-large-patch14
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2357
+  Similarity threshold:      0.8
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      187
+  False Positives:     375
+  False Negatives:     208
+  Total Expected:      369
+
+Scores:
+  Precision:  0.3327 (33.3%)
+  Recall:     0.5068 (50.7%)
+  F1 Score:   0.4017 (40.2%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: openai/clip-vit-large-patch14
+Method: Multi-ref (max, min_refs=3, margin=0.1)
+======================================================================
+Date: 2026-01-02 11:23:33
+
+Configuration:
+  Embedding model:           openai/clip-vit-large-patch14
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2356
+  Similarity threshold:      0.85
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      160
+  False Positives:     434
+  False Negatives:     223
+  Total Expected:      369
+
+Scores:
+  Precision:  0.2694 (26.9%)
+  Recall:     0.4336 (43.4%)
+  F1 Score:   0.3323 (33.2%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: openai/clip-vit-large-patch14
+Method: Multi-ref (max, min_refs=3, margin=0.15)
+======================================================================
+Date: 2026-01-02 11:41:47
+
+Configuration:
+  Embedding model:           openai/clip-vit-large-patch14
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2359
+  Similarity threshold:      0.85
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:      163
+  False Positives:     410
+  False Negatives:     220
+  Total Expected:      369
+
+Scores:
+  Precision:  0.2845 (28.4%)
+  Recall:     0.4417 (44.2%)
+  F1 Score:   0.3461 (34.6%)
+
+======================================================================
+TEST: MULTI-REF MATCHING
+Model: openai/clip-vit-large-patch14
+Method: Multi-ref (max, min_refs=3, margin=0.15)
+======================================================================
+Date: 2026-01-02 12:00:00
+
+Configuration:
+  Embedding model:           openai/clip-vit-large-patch14
+  Reference logos:           20
+  Refs per logo:             10
+  Total reference embeddings:189
+  Positive samples/logo:     20
+  Negative samples/logo:     100
+  Test images processed:     2363
+  Similarity threshold:      0.9
+  DETR threshold:            0.5
+  Random seed:               42
+
+Results:
+  True Positives:       84
+  False Positives:      69
+  False Negatives:     288
+  Total Expected:      369
+
+Scores:
+  Precision:  0.5490 (54.9%)
+  Recall:     0.2276 (22.8%)
+  F1 Score:   0.3218 (32.2%)
+