Update README with new test parameters and dataset setup

- Add detailed instructions for LogoDet-3K dataset placement
- Document all test script parameters including new options:
  - simple matching method
  - --output-file for clean results output
  - --use-max-similarity, --positive-samples, --negative-samples
- Add section on running comparison tests with shell script
- Update project structure to include run_comparison_tests.sh

README.md

@@ -32,13 +32,33 @@ pip install -r requirements.txt
 
 ### Prepare Test Data
 
-First, prepare the test database with logo mappings:
+The test framework requires the **LogoDet-3K** dataset. Download it and place it in the project directory:
+
+```
+logo_test/
+├── LogoDet-3K/           # Dataset directory (required)
+│   ├── Clothes/          # Category directories
+│   │   ├── Adidas/       # Brand directories with images + XML annotations
+│   │   ├── Nike/
+│   │   └── ...
+│   ├── Electronic/
+│   ├── Food/
+│   └── ...
+```
+
+The dataset should contain images with corresponding Pascal VOC format XML annotation files that define logo bounding boxes.
+
+Then run the preparation script:
 
 ```bash
 uv run python prepare_test_data.py
 ```
 
-This creates `test_data_mapping.db` with ground truth mappings between test images and logos.
+This script:
+1. Scans `LogoDet-3K/` for images and XML annotation files
+2. Extracts cropped logo regions using bounding box data → saves to `reference_logos/`
+3. Copies full images → saves to `test_images/`
+4. Creates `test_data_mapping.db` SQLite database with ground truth mappings
 
 ### Run Detection Tests
 
@@ -64,14 +84,33 @@ uv run python test_logo_detection.py -n 50 --seed 42
 | `-n, --num-logos` | 10 | Number of reference logos to sample |
 | `-t, --threshold` | 0.7 | CLIP similarity threshold |
 | `-d, --detr-threshold` | 0.5 | DETR detection confidence threshold |
-| `--matching-method` | margin | Matching method: `margin` or `multi-ref` |
-| `--margin` | 0.05 | Margin over second-best match (margin method) |
-| `--min-matching-refs` | 1 | Min refs that must match (multi-ref method) |
+| `--matching-method` | margin | Matching method: `simple`, `margin`, or `multi-ref` |
+| `--margin` | 0.05 | Margin over second-best match (margin/multi-ref) |
 | `--refs-per-logo` | 3 | Reference images per logo |
+| `--min-matching-refs` | 1 | Min refs that must match (multi-ref only) |
+| `--use-max-similarity` | False | Use max instead of mean similarity (multi-ref only) |
+| `--positive-samples` | 5 | Positive test images per logo |
+| `--negative-samples` | 20 | Negative test images per logo |
 | `-s, --seed` | None | Random seed for reproducibility |
+| `--output-file` | None | Append results summary to file (clean output) |
+
+**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
 
 See `--help` for all options.
 
+### Run Comparison Tests
+
+To compare all matching methods with consistent parameters:
+
+```bash
+./run_comparison_tests.sh
+```
+
+This runs all four matching configurations (simple, margin, multi-ref mean, multi-ref max) and saves clean results to `comparison_results.txt`.
+
 ## Project Structure
 
 ```
@@ -79,9 +118,11 @@ logo_test/
 ├── logo_detection_detr.py      # Core detection library (DetectLogosDETR class)
 ├── test_logo_detection.py      # Test script for accuracy evaluation
 ├── prepare_test_data.py        # Script to prepare test database
+├── run_comparison_tests.sh     # Script to run all matching methods
 ├── test_data_mapping.db        # SQLite database with ground truth
 ├── reference_logos/            # Reference logo images (not in git)
 ├── test_images/                # Test images (not in git)
+├── LogoDet-3K/                 # Source dataset (not in git)
 ├── logo_detection_detr_usage.md        # API usage guide
 └── logo_detection_test_methodology.md  # Test methodology documentation
 ```