Update README with recommended settings and test results

Add comprehensive recommendations section based on LogoDet-3K testing:
- Optimal parameter settings table (multi-ref, max aggregation, CLIP model)
- Performance benchmarks for refs-per-logo (1-10 refs)
- Matching method comparison (simple vs margin vs multi-ref)
- Embedding model comparison (CLIP vs DINOv2)
- Preprocessing mode comparison (default vs letterbox vs stretch)

README.md

@@ -2,6 +2,83 @@
 
 A testing framework for evaluating logo detection accuracy using DETR (DEtection TRansformer) and CLIP (Contrastive Language-Image Pre-training) models.
 
+## Recommended Settings
+
+Based on extensive testing with the LogoDet-3K dataset, these are the optimal settings:
+
+| Parameter | Recommended Value | Notes |
+|-----------|-------------------|-------|
+| **Matching Method** | `multi-ref` | Best balance of precision and recall |
+| **Similarity Aggregation** | `max` (default) | Max outperforms mean aggregation |
+| **Embedding Model** | `openai/clip-vit-large-patch14` | Significantly outperforms DINOv2 |
+| **CLIP Threshold** | `0.70` | Good precision/recall balance |
+| **DETR Threshold** | `0.50` | Default detection confidence |
+| **Margin** | `0.05` | Reduces false positives |
+| **Refs per Logo** | `7-10` | More references = better accuracy |
+| **Preprocessing** | `default` | Best precision; letterbox/stretch hurt precision |
+
+**Example command with recommended settings:**
+```bash
+uv run python test_logo_detection.py \
+    --matching-method multi-ref \
+    --refs-per-logo 10 \
+    --threshold 0.70 \
+    --margin 0.05 \
+    --use-max-similarity
+```
+
+### Performance Benchmarks
+
+With recommended settings (multi-ref max, threshold 0.70, margin 0.05):
+
+| Refs/Logo | Precision | Recall | F1 Score |
+|-----------|-----------|--------|----------|
+| 1 | 45.8% | 65.9% | 54.0% |
+| 3 | 40.5% | 72.4% | 51.9% |
+| 5 | 47.2% | 72.6% | 57.2% |
+| 7 | **51.0%** | **79.9%** | **62.3%** |
+| 10 | 50.2% | 81.6% | 62.1% |
+
+**Key findings:**
+- More reference images per logo consistently improves recall
+- 7+ refs provides the best precision/recall balance
+- Diminishing returns beyond 10 refs
+
+### Matching Method Comparison
+
+| Method | Precision | Recall | F1 | Use Case |
+|--------|-----------|--------|-----|----------|
+| `simple` | 1.3% | 203%* | 2.5% | Not recommended (too many FPs) |
+| `margin` | 69.8% | 16.3% | 26.4% | High precision, low recall |
+| `multi-ref` (mean) | 51.8% | 63.1% | 56.9% | Balanced |
+| `multi-ref` (max) | **51.8%** | **75.3%** | **61.4%** | **Best overall** |
+
+*Simple method returns all matches above threshold, causing many duplicates.
+
+### Embedding Model Comparison
+
+| Model | Precision | Recall | F1 | Recommendation |
+|-------|-----------|--------|-----|----------------|
+| `openai/clip-vit-large-patch14` | **49.1%** | **77.0%** | **59.9%** | **Recommended** |
+| `facebook/dinov2-small` | 22.4% | 42.8% | 29.5% | Not recommended |
+| `facebook/dinov2-large` | 32.2% | 28.5% | 30.2% | Not recommended |
+
+CLIP significantly outperforms DINOv2 for logo matching tasks.
+
+### Preprocessing Mode Comparison
+
+| Mode | Precision | Recall | F1 | Notes |
+|------|-----------|--------|-----|-------|
+| `default` | **50.2%** | 81.6% | 62.1% | **Recommended** - best precision |
+| `letterbox` | 42.4% | 119%* | 62.6% | Higher recall but worse precision |
+| `stretch` | 34.5% | 113%* | 52.9% | Not recommended |
+
+*Recall >100% indicates multiple detections per expected logo.
+
+**Recommendation:** Use `default` preprocessing. While letterbox shows marginally higher F1, it has significantly worse precision (more false positives).
+
+---
+
 ## Overview
 
 This project provides tools to:
@@ -97,9 +174,9 @@ uv run python test_logo_detection.py -n 50 --seed 42
 | `--clear-cache` | False | Clear embedding cache before running |
 
 **Matching Methods:**
-- `simple` - Returns all logos above threshold (baseline, most permissive)
-- `margin` - Requires margin over second-best match (reduces false positives)
-- `multi-ref` - Aggregates scores across multiple reference images per logo
+- `simple` - Returns all logos above threshold (not recommended - too many false positives)
+- `margin` - Requires margin over second-best match (high precision, low recall)
+- `multi-ref` - **Recommended.** Aggregates scores across multiple reference images per logo
 
 See `--help` for all options.
 
@@ -114,13 +191,18 @@ See `--help` for all options.
 
 # Compare embedding models (CLIP vs DINOv2)
 ./run_model_comparison.sh
+
+# Test different refs-per-logo values
+./run_refs_per_logo_test.sh
 ```
 
 | Script | Purpose | Output File |
 |--------|---------|-------------|
-| `run_comparison_tests.sh` | Compare all 4 matching methods | `comparison_results.txt` |
-| `run_threshold_tests.sh` | Test threshold/margin combinations | `threshold_test_results.txt` |
-| `run_model_comparison.sh` | Compare CLIP vs DINOv2 models | `model_comparison_results.txt` |
+| `run_comparison_tests.sh` | Compare matching methods | `test_results/comparison_*.txt` |
+| `run_threshold_tests.sh` | Test threshold/margin combinations | `test_results/threshold_*.txt` |
+| `run_model_comparison.sh` | Compare CLIP vs DINOv2 models | `test_results/model_comparison_results.txt` |
+| `run_refs_per_logo_test.sh` | Test refs-per-logo values | `test_results/refs_per_logo_analysis.txt` |
+| `run_preprocess_test.sh` | Compare preprocessing modes | `test_results/preprocessing_comparison.txt` |
 
 ## Project Structure