Initial commit: Logo detection test framework

Add DETR+CLIP based logo detection library and test framework: - DetectLogosDETR class for logo detection and matching - Test script with margin-based and multi-ref matching methods - Data preparation script for test database - Documentation for API usage and test methodology
2025-12-31 10:42:36 -05:00
commit ddccf653d2
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--- a/.gitignore
+++ b/.gitignore
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+# Python-generated files
+__pycache__/
+*.py[oc]
+build/
+dist/
+wheels/
+*.egg-info
+
+# Virtual environments
+.venv
+
+# Image directories
+reference_logos/
+test_images/
+
+# Image files
+*.jpg
+*.jpeg
+*.png
+*.gif
+*.bmp
+*.webp
+
+# Database and data files
+*.db
+*.json
+*.pkl
+
+# Cache files
+.embedding_cache.pkl
+
+# IDE
+.idea/
+.vscode/
+
+# Results files
+results*.txt
+sample_results.txt
--- a/.python-version
+++ b/.python-version
@ -0,0 +1 @@
+3.12
--- a/CLAUDE.md
+++ b/CLAUDE.md
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+# CLAUDE.md
+
+This file provides guidance to Claude Code (claude.ai/code) when working with code in this repository.
+
+## Project Overview
+
+Logo detection system using deep learning models:
+- **DETR** (DEtection TRansformer) for logo region detection
+- **CLIP** (Contrastive Language-Image Pre-training) for feature extraction and matching
+
+## Development Commands
+
+```bash
+# Install dependencies (uses uv package manager)
+uv sync
+
+# Run main script
+uv run python main.py
+
+# Run logo detection module directly
+uv run python logo_detection_detr.py
+```
+
+## Architecture
+
+### Core Module: `logo_detection_detr.py`
+
+The `DetectLogosDETR` class provides the main detection pipeline:
+
+1. **Detection Flow**: OpenCV image (BGR) → DETR detects bounding boxes → CLIP extracts embeddings for each region
+2. **Matching Flow**: Compare detected embeddings against reference logo embeddings using cosine similarity
+
+**Key Methods:**
+- `detect(image)` - Detect logos, returns boxes + CLIP embeddings
+- `get_embedding(image)` - Get CLIP embedding for a reference logo
+- `compare_embeddings(emb1, emb2)` - Cosine similarity between embeddings
+- `detect_and_match(image, references, threshold)` - Combined detection and matching
+
+### Model Configuration
+
+Models are resolved in this order:
+1. Absolute path if provided
+2. Local directory from environment variables (`LOGO_DETR_MODEL_DIR`, `LOGO_CLIP_MODEL_DIR`)
+3. Default local paths: `models/logo_detection/detr`, `models/logo_detection/clip`
+4. HuggingFace download as fallback
+
+Default models:
+- DETR: `Pravallika6/detr-finetuned-logo-detection_v2`
+- CLIP: `openai/clip-vit-large-patch14`
+
+### Reference Dataset
+
+`LogoDet-3K/` contains logo images organized by category: Clothes, Electronic, Food, Leisure, Medical, Necessities, Others, Sports, Transportation.
--- a/README.md
+++ b/README.md
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+# Logo Detection Test Framework
+
+A testing framework for evaluating logo detection accuracy using DETR (DEtection TRansformer) and CLIP (Contrastive Language-Image Pre-training) models.
+
+## Overview
+
+This project provides tools to:
+- Detect logos in images using a fine-tuned DETR model
+- Match detected logos against reference images using CLIP embeddings
+- Evaluate detection accuracy with precision, recall, and F1 metrics
+
+## Architecture
+
+The system uses a two-stage pipeline:
+
+1. **DETR** - Identifies potential logo regions (bounding boxes) in images
+2. **CLIP** - Extracts feature embeddings for each detected region and compares against reference logos
+
+## Installation
+
+Requires Python 3.12+. Uses [uv](https://github.com/astral-sh/uv) for package management.
+
+```bash
+# Install dependencies
+uv sync
+
+# Or using pip
+pip install -r requirements.txt
+```
+
+## Usage
+
+### Prepare Test Data
+
+First, prepare the test database with logo mappings:
+
+```bash
+uv run python prepare_test_data.py
+```
+
+This creates `test_data_mapping.db` with ground truth mappings between test images and logos.
+
+### Run Detection Tests
+
+```bash
+# Basic test with default settings (margin-based matching)
+uv run python test_logo_detection.py
+
+# Test with more logos and custom threshold
+uv run python test_logo_detection.py -n 20 --threshold 0.75
+
+# Use multi-ref matching method
+uv run python test_logo_detection.py --matching-method multi-ref \
+    --refs-per-logo 5 --min-matching-refs 2
+
+# Reproducible test with seed
+uv run python test_logo_detection.py -n 50 --seed 42
+```
+
+### Key Parameters
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `-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) |
+| `--refs-per-logo` | 3 | Reference images per logo |
+| `-s, --seed` | None | Random seed for reproducibility |
+
+See `--help` for all options.
+
+## Project Structure
+
+```
+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
+├── test_data_mapping.db        # SQLite database with ground truth
+├── reference_logos/            # Reference logo images (not in git)
+├── test_images/                # Test images (not in git)
+├── logo_detection_detr_usage.md        # API usage guide
+└── logo_detection_test_methodology.md  # Test methodology documentation
+```
+
+## Accuracy Improvement Techniques
+
+The framework implements several techniques to improve detection accuracy:
+
+1. **Non-Maximum Suppression (NMS)** - Removes overlapping duplicate detections
+2. **Minimum Box Size Filtering** - Filters out noise from tiny detections
+3. **Confidence Threshold Filtering** - Removes low-confidence detections
+4. **Multiple Reference Images** - Uses multiple refs per logo for robust matching
+5. **Margin-Based Matching** - Requires confidence margin over second-best match
+6. **Multi-Ref Matching** - Aggregates similarity scores across references
+7. **Embedding Caching** - Caches embeddings to avoid recomputation
+
+## Models
+
+The framework uses:
+- **DETR**: `Pravallika6/detr-finetuned-logo-detection_v2`
+- **CLIP**: `openai/clip-vit-large-patch14`
+
+Models are automatically downloaded from HuggingFace on first run and cached in `~/.cache/huggingface/`.
+
+## Documentation
+
+- [API Usage Guide](logo_detection_detr_usage.md) - How to use the DetectLogosDETR class
+- [Test Methodology](logo_detection_test_methodology.md) - Detailed explanation of test framework and tuning
+
+## License
+
+MIT
--- a/logo_detection_detr.py
+++ b/logo_detection_detr.py
@ -0,0 +1,556 @@
+"""
+Logo detection using DETR for object detection and CLIP for feature matching.
+
+This module provides a class for detecting logos in images using:
+1. DETR (DEtection TRansformer) for initial logo region detection
+2. CLIP (Contrastive Language-Image Pre-training) for feature extraction and matching
+
+The class supports caching of embeddings for efficient reprocessing.
+The class automatically uses local models if available, otherwise falls back to HuggingFace.
+"""
+
+import os
+import torch
+import torch.nn.functional as F
+from transformers import pipeline, CLIPProcessor, CLIPModel
+from PIL import Image
+import cv2
+import numpy as np
+from pathlib import Path
+from typing import List, Tuple, Dict, Optional, Any
+
+
+class DetectLogosDETR:
+    """
+    Logo detection class using DETR and CLIP models.
+
+    This class detects logos in images by:
+    1. Using DETR to find potential logo regions (bounding boxes)
+    2. Extracting CLIP embeddings for each detected region
+    3. Comparing embeddings with reference logos for identification
+
+    The class automatically checks for local models before downloading from HuggingFace.
+    """
+
+    def __init__(
+        self,
+        logger,
+        detr_model: str = "Pravallika6/detr-finetuned-logo-detection_v2",
+        #clip_model: str = "openai/clip-vit-base-patch32",
+        clip_model: str = "openai/clip-vit-large-patch14",
+        detr_threshold: float = 0.5,
+        min_box_size: int = 20,
+        nms_iou_threshold: float = 0.5,
+    ):
+        """
+        Initialize DETR and CLIP models.
+
+        The class will automatically check for local models in the default directories
+        before downloading from HuggingFace. You can override this by providing absolute
+        paths to local models.
+
+        Args:
+            logger: Logger instance for logging
+            detr_model: HuggingFace model name or local path for DETR object detection
+            clip_model: HuggingFace model name or local path for CLIP embeddings
+            detr_threshold: Confidence threshold for DETR detections (0-1)
+            min_box_size: Minimum width/height in pixels for detected boxes (filters noise)
+            nms_iou_threshold: IoU threshold for Non-Maximum Suppression
+        """
+        self.logger = logger
+        self.detr_threshold = detr_threshold
+        self.min_box_size = min_box_size
+        self.nms_iou_threshold = nms_iou_threshold
+
+        # Set device
+        self.device_str = "cuda:0" if torch.cuda.is_available() else "cpu"
+        self.device_index = 0 if torch.cuda.is_available() else -1
+        self.device = torch.device(self.device_str)
+
+        self.logger.info(f"Initializing DetectLogosDETR on device: {self.device_str}")
+
+        # Get default model directories from environment variables
+        default_detr_dir = os.environ.get('LOGO_DETR_MODEL_DIR', 'models/logo_detection/detr')
+        default_clip_dir = os.environ.get('LOGO_CLIP_MODEL_DIR', 'models/logo_detection/clip')
+
+        # Resolve DETR model path (check local first, then use HuggingFace name)
+        detr_model_path = self._resolve_model_path(
+            detr_model, default_detr_dir, "DETR"
+        )
+
+        # Initialize DETR pipeline for logo detection
+        self.logger.info(f"Loading DETR model: {detr_model_path}")
+        self.detr_pipe = pipeline(
+            task="object-detection",
+            model=detr_model_path,
+            device=self.device_index,
+            use_fast=True,
+        )
+
+        # Resolve CLIP model path (check local first, then use HuggingFace name)
+        clip_model_path = self._resolve_model_path(
+            clip_model, default_clip_dir, "CLIP"
+        )
+
+        # Initialize CLIP model for feature extraction
+        self.logger.info(f"Loading CLIP model: {clip_model_path}")
+        self.clip_model = CLIPModel.from_pretrained(clip_model_path).to(self.device)
+        self.clip_processor = CLIPProcessor.from_pretrained(clip_model_path)
+
+        self.logger.info("DetectLogosDETR initialization complete")
+
+    def _resolve_model_path(
+        self, model_name_or_path: str, default_local_dir: str, model_type: str
+    ) -> str:
+        """
+        Resolve model path, checking for local models before using HuggingFace.
+
+        Args:
+            model_name_or_path: HuggingFace model name or absolute path
+            default_local_dir: Default local directory to check
+            model_type: Type of model (for logging, e.g., "DETR" or "CLIP")
+
+        Returns:
+            Resolved model path (local path or HuggingFace model name)
+        """
+        # If it's an absolute path, use it directly
+        if os.path.isabs(model_name_or_path):
+            if os.path.exists(model_name_or_path):
+                self.logger.info(
+                    f"{model_type} model: Using local model at {model_name_or_path}"
+                )
+                return model_name_or_path
+            else:
+                self.logger.warning(
+                    f"{model_type} model: Local path {model_name_or_path} does not exist, "
+                    f"falling back to HuggingFace"
+                )
+                return model_name_or_path
+
+        # Check if default local directory exists
+        if os.path.exists(default_local_dir):
+            # Verify it's a valid model directory (has config.json)
+            config_file = os.path.join(default_local_dir, "config.json")
+            if os.path.exists(config_file):
+                abs_path = os.path.abspath(default_local_dir)
+                self.logger.info(
+                    f"{model_type} model: Found local model at {abs_path}"
+                )
+                return abs_path
+            else:
+                self.logger.warning(
+                    f"{model_type} model: Local directory {default_local_dir} exists but "
+                    f"is not a valid model (missing config.json)"
+                )
+
+        # Use HuggingFace model name
+        self.logger.info(
+            f"{model_type} model: No local model found, will download from HuggingFace: "
+            f"{model_name_or_path}"
+        )
+        return model_name_or_path
+
+    def detect(self, image: np.ndarray) -> List[Dict[str, Any]]:
+        """
+        Detect logos in an image and return bounding boxes with CLIP embeddings.
+
+        Args:
+            image: OpenCV image (BGR format, numpy array)
+
+        Returns:
+            List of dictionaries, each containing:
+                - 'box': dict with 'xmin', 'ymin', 'xmax', 'ymax' (pixel coordinates)
+                - 'score': DETR confidence score (float 0-1)
+                - 'embedding': CLIP feature embedding (torch.Tensor)
+                - 'label': DETR predicted label (string)
+        """
+        # Convert OpenCV BGR to RGB PIL Image
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        pil_image = Image.fromarray(image_rgb)
+
+        # Run DETR detection
+        predictions = self.detr_pipe(pil_image)
+
+        # Filter by threshold and size, then add CLIP embeddings
+        detections = []
+        for pred in predictions:
+            score = pred.get("score", 0.0)
+            if score < self.detr_threshold:
+                continue
+
+            box = pred.get("box", {})
+            xmin = box.get("xmin", 0)
+            ymin = box.get("ymin", 0)
+            xmax = box.get("xmax", 0)
+            ymax = box.get("ymax", 0)
+
+            # Filter by minimum box size
+            box_width = xmax - xmin
+            box_height = ymax - ymin
+            if box_width < self.min_box_size or box_height < self.min_box_size:
+                continue
+
+            # Extract bounding box region
+            bbox_crop = pil_image.crop((xmin, ymin, xmax, ymax))
+
+            # Get CLIP embedding for this region
+            embedding = self._get_clip_embedding_pil(bbox_crop)
+
+            detections.append(
+                {
+                    "box": {"xmin": xmin, "ymin": ymin, "xmax": xmax, "ymax": ymax},
+                    "score": score,
+                    "embedding": embedding,
+                    "label": pred.get("label", "logo"),
+                }
+            )
+
+        # Apply Non-Maximum Suppression to remove overlapping detections
+        detections = self._apply_nms(detections, self.nms_iou_threshold)
+
+        self.logger.debug(f"Detected {len(detections)} logos (threshold: {self.detr_threshold})")
+        return detections
+
+    def _apply_nms(self, predictions: List[Dict], iou_threshold: float) -> List[Dict]:
+        """
+        Apply Non-Maximum Suppression to remove overlapping detections.
+
+        Args:
+            predictions: List of prediction dictionaries with 'box' and 'score'
+            iou_threshold: IoU threshold for considering boxes as overlapping
+
+        Returns:
+            Filtered list of predictions after NMS
+        """
+        if len(predictions) == 0:
+            return []
+
+        # Extract boxes and scores
+        boxes = []
+        scores = []
+        for pred in predictions:
+            box = pred.get("box", {})
+            boxes.append([
+                box.get("xmin", 0),
+                box.get("ymin", 0),
+                box.get("xmax", 0),
+                box.get("ymax", 0)
+            ])
+            scores.append(pred.get("score", 0.0))
+
+        # Convert to numpy arrays
+        boxes = np.array(boxes, dtype=np.float32)
+        scores = np.array(scores, dtype=np.float32)
+
+        # Sort by scores (descending)
+        sorted_indices = np.argsort(scores)[::-1]
+
+        keep_indices = []
+        while len(sorted_indices) > 0:
+            # Keep the box with highest score
+            current_idx = sorted_indices[0]
+            keep_indices.append(current_idx)
+
+            if len(sorted_indices) == 1:
+                break
+
+            # Calculate IoU with remaining boxes
+            current_box = boxes[current_idx]
+            remaining_boxes = boxes[sorted_indices[1:]]
+
+            ious = self._calculate_iou_batch(current_box, remaining_boxes)
+
+            # Keep only boxes with IoU below threshold
+            mask = ious < iou_threshold
+            sorted_indices = sorted_indices[1:][mask]
+
+        # Return predictions for kept indices
+        return [predictions[i] for i in keep_indices]
+
+    def _calculate_iou_batch(self, box: np.ndarray, boxes: np.ndarray) -> np.ndarray:
+        """
+        Calculate IoU between one box and multiple boxes.
+
+        Args:
+            box: Single box [xmin, ymin, xmax, ymax]
+            boxes: Multiple boxes [[xmin, ymin, xmax, ymax], ...]
+
+        Returns:
+            Array of IoU values
+        """
+        # Calculate intersection coordinates
+        x1 = np.maximum(box[0], boxes[:, 0])
+        y1 = np.maximum(box[1], boxes[:, 1])
+        x2 = np.minimum(box[2], boxes[:, 2])
+        y2 = np.minimum(box[3], boxes[:, 3])
+
+        # Calculate intersection area
+        intersection = np.maximum(0, x2 - x1) * np.maximum(0, y2 - y1)
+
+        # Calculate union area
+        box_area = (box[2] - box[0]) * (box[3] - box[1])
+        boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
+        union = box_area + boxes_area - intersection
+
+        # Calculate IoU
+        iou = intersection / (union + 1e-6)  # Add small epsilon to avoid division by zero
+
+        return iou
+
+    def get_embedding(self, image: np.ndarray) -> torch.Tensor:
+        """
+        Get CLIP embedding for a reference logo image.
+
+        This method is used to compute embeddings for reference logos
+        that will be compared against detected regions.
+
+        Args:
+            image: OpenCV image (BGR format, numpy array)
+
+        Returns:
+            Normalized CLIP feature embedding (torch.Tensor, shape: [1, 512])
+        """
+        # Convert OpenCV BGR to RGB PIL Image
+        image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+        pil_image = Image.fromarray(image_rgb)
+
+        return self._get_clip_embedding_pil(pil_image)
+
+    def _get_clip_embedding_pil(self, pil_image: Image.Image) -> torch.Tensor:
+        """
+        Internal method to get CLIP embedding from PIL image.
+
+        Args:
+            pil_image: PIL Image (RGB format)
+
+        Returns:
+            Normalized CLIP feature embedding (torch.Tensor)
+        """
+        # Process image through CLIP
+        inputs = self.clip_processor(images=pil_image, return_tensors="pt").to(self.device)
+
+        with torch.no_grad():
+            features = self.clip_model.get_image_features(**inputs)
+            # Normalize for cosine similarity
+            features = F.normalize(features, dim=-1)
+
+        return features
+
+    def compare_embeddings(
+        self, embedding1: torch.Tensor, embedding2: torch.Tensor
+    ) -> float:
+        """
+        Compute cosine similarity between two CLIP embeddings.
+
+        Args:
+            embedding1: First CLIP embedding (torch.Tensor)
+            embedding2: Second CLIP embedding (torch.Tensor)
+
+        Returns:
+            Cosine similarity score (float, range: -1 to 1, typically 0 to 1)
+        """
+        # Ensure tensors are on the same device
+        if embedding1.device != embedding2.device:
+            embedding2 = embedding2.to(embedding1.device)
+
+        # Compute cosine similarity
+        similarity = F.cosine_similarity(embedding1, embedding2, dim=-1)
+
+        # Return as Python float
+        return similarity.item()
+
+    def find_best_match(
+        self,
+        detected_embedding: torch.Tensor,
+        reference_embeddings: List[Tuple[str, torch.Tensor]],
+        similarity_threshold: float = 0.7,
+    ) -> Optional[Tuple[str, float]]:
+        """
+        Find the best matching reference logo for a detected embedding.
+
+        Args:
+            detected_embedding: CLIP embedding from detected logo region
+            reference_embeddings: List of (label, embedding) tuples for reference logos
+            similarity_threshold: Minimum similarity to consider a match (0-1)
+
+        Returns:
+            Tuple of (label, similarity) for best match, or None if no match above threshold
+        """
+        if not reference_embeddings:
+            return None
+
+        best_similarity = -1.0
+        best_label = None
+
+        for label, ref_embedding in reference_embeddings:
+            similarity = self.compare_embeddings(detected_embedding, ref_embedding)
+
+            if similarity > best_similarity:
+                best_similarity = similarity
+                best_label = label
+
+        if best_similarity >= similarity_threshold:
+            return (best_label, best_similarity)
+        else:
+            return None
+
+    def find_best_match_multi_ref(
+        self,
+        detected_embedding: torch.Tensor,
+        reference_embeddings: Dict[str, List[torch.Tensor]],
+        similarity_threshold: float = 0.85,
+        min_matching_refs: int = 1,
+        use_mean_similarity: bool = True,
+    ) -> Optional[Tuple[str, float, int]]:
+        """
+        Find the best matching reference logo using multiple reference embeddings per logo.
+
+        This method improves accuracy by using multiple reference images for each logo
+        and requiring consistency across references.
+
+        Args:
+            detected_embedding: CLIP embedding from detected logo region
+            reference_embeddings: Dict mapping logo name to list of embeddings
+            similarity_threshold: Minimum similarity to consider a match (0-1)
+            min_matching_refs: Minimum number of references that must match above threshold
+            use_mean_similarity: If True, use mean similarity across all refs; if False, use max
+
+        Returns:
+            Tuple of (label, similarity, num_matching_refs) for best match,
+            or None if no match meets criteria
+        """
+        if not reference_embeddings:
+            return None
+
+        best_score = -1.0
+        best_label = None
+        best_num_matches = 0
+
+        for label, ref_embedding_list in reference_embeddings.items():
+            if not ref_embedding_list:
+                continue
+
+            # Calculate similarity to each reference embedding
+            similarities = []
+            for ref_embedding in ref_embedding_list:
+                sim = self.compare_embeddings(detected_embedding, ref_embedding)
+                similarities.append(sim)
+
+            # Count how many references match above threshold
+            num_matches = sum(1 for s in similarities if s >= similarity_threshold)
+
+            # Calculate aggregate score
+            if use_mean_similarity:
+                score = sum(similarities) / len(similarities)
+            else:
+                score = max(similarities)
+
+            # Check if this logo meets the minimum matching refs requirement
+            if num_matches >= min_matching_refs and score > best_score:
+                best_score = score
+                best_label = label
+                best_num_matches = num_matches
+
+        if best_label is not None and best_score >= similarity_threshold:
+            return (best_label, best_score, best_num_matches)
+        else:
+            return None
+
+    def find_best_match_with_margin(
+        self,
+        detected_embedding: torch.Tensor,
+        reference_embeddings: List[Tuple[str, torch.Tensor]],
+        similarity_threshold: float = 0.85,
+        margin: float = 0.05,
+    ) -> Optional[Tuple[str, float]]:
+        """
+        Find best match with a confidence margin over the second-best match.
+
+        This reduces false positives by requiring the best match to be
+        significantly better than alternatives.
+
+        Args:
+            detected_embedding: CLIP embedding from detected logo region
+            reference_embeddings: List of (label, embedding) tuples for reference logos
+            similarity_threshold: Minimum similarity to consider a match (0-1)
+            margin: Required margin between best and second-best match
+
+        Returns:
+            Tuple of (label, similarity) for best match, or None if no confident match
+        """
+        if not reference_embeddings:
+            return None
+
+        # Calculate all similarities
+        similarities = []
+        for label, ref_embedding in reference_embeddings:
+            sim = self.compare_embeddings(detected_embedding, ref_embedding)
+            similarities.append((label, sim))
+
+        # Sort by similarity descending
+        similarities.sort(key=lambda x: x[1], reverse=True)
+
+        best_label, best_sim = similarities[0]
+
+        # Check if best is above threshold
+        if best_sim < similarity_threshold:
+            return None
+
+        # Check margin against second best (if exists)
+        if len(similarities) > 1:
+            second_best_sim = similarities[1][1]
+            if best_sim - second_best_sim < margin:
+                return None  # Not confident enough
+
+        return (best_label, best_sim)
+
+    def detect_and_match(
+        self,
+        image: np.ndarray,
+        reference_embeddings: List[Tuple[str, torch.Tensor]],
+        similarity_threshold: float = 0.7,
+    ) -> List[Dict[str, Any]]:
+        """
+        Detect logos and match them against reference embeddings in one step.
+
+        This is a convenience method that combines detection and matching.
+
+        Args:
+            image: OpenCV image (BGR format, numpy array)
+            reference_embeddings: List of (label, embedding) tuples for reference logos
+            similarity_threshold: Minimum similarity to consider a match (0-1)
+
+        Returns:
+            List of matched detections, each containing:
+                - 'box': bounding box coordinates
+                - 'detr_score': DETR confidence score
+                - 'clip_similarity': CLIP similarity score
+                - 'label': matched reference logo label
+        """
+        # Detect all logos
+        detections = self.detect(image)
+
+        # Match each detection against references
+        matched_detections = []
+        for detection in detections:
+            match_result = self.find_best_match(
+                detection["embedding"], reference_embeddings, similarity_threshold
+            )
+
+            if match_result is not None:
+                label, similarity = match_result
+                matched_detections.append(
+                    {
+                        "box": detection["box"],
+                        "detr_score": detection["score"],
+                        "clip_similarity": similarity,
+                        "label": label,
+                    }
+                )
+
+        self.logger.debug(
+            f"Matched {len(matched_detections)}/{len(detections)} detections "
+            f"(threshold: {similarity_threshold})"
+        )
+
+        return matched_detections
--- a/logo_detection_detr_usage.md
+++ b/logo_detection_detr_usage.md
@ -0,0 +1,301 @@
+# DetectLogosDETR Class Usage Guide
+
+## Overview
+
+The `DetectLogosDETR` class provides logo detection using:
+- **DETR** (DEtection TRansformer) for initial logo region detection
+- **CLIP** (Contrastive Language-Image Pre-training) for feature embeddings and matching
+
+## Key Features
+
+### 1. **Constructor** - Initializes models with CUDA support
+
+```python
+from scan_utils.logo_detection_detr import DetectLogosDETR
+
+detector = DetectLogosDETR(logger, detr_threshold=0.5)
+```
+
+- Automatically detects and uses CUDA if available
+- Loads DETR for logo region detection
+- Loads CLIP for feature embeddings
+- `detr_threshold`: Confidence threshold for DETR detections (0-1, default: 0.5)
+
+### 2. **Main Detection Methods**
+
+#### `detect(image)` - Detect logos and return embeddings
+
+```python
+detections = detector.detect(opencv_image)
+# Returns: [{'box': {...}, 'score': 0.95, 'embedding': tensor, 'label': 'logo'}, ...]
+```
+
+Returns a list of dictionaries, each containing:
+- `box`: Dictionary with `xmin`, `ymin`, `xmax`, `ymax` (pixel coordinates)
+- `score`: DETR confidence score (float 0-1)
+- `embedding`: CLIP feature embedding (torch.Tensor)
+- `label`: DETR predicted label (string)
+
+#### `get_embedding(image)` - Get embedding for reference logos
+
+```python
+embedding = detector.get_embedding(reference_logo_image)
+# For caching reference logo embeddings
+```
+
+- Takes OpenCV image (BGR format)
+- Returns normalized CLIP embedding (torch.Tensor, shape: [1, 512])
+- Used to compute embeddings for reference logos that will be cached
+
+#### `compare_embeddings(emb1, emb2)` - Compute cosine similarity
+
+```python
+similarity = detector.compare_embeddings(detected_emb, reference_emb)
+# Returns: float (0-1, higher = more similar)
+```
+
+- Compares two CLIP embeddings
+- Returns cosine similarity score (float, range: -1 to 1, typically 0 to 1)
+
+### 3. **Convenience Methods**
+
+#### `find_best_match()` - Find best matching reference logo
+
+```python
+match = detector.find_best_match(
+    detected_embedding,
+    reference_embeddings,
+    similarity_threshold=0.7
+)
+# Returns: (label, similarity) or None
+```
+
+**Parameters:**
+- `detected_embedding`: CLIP embedding from detected logo region
+- `reference_embeddings`: List of (label, embedding) tuples for reference logos
+- `similarity_threshold`: Minimum similarity to consider a match (0-1, default: 0.7)
+
+**Returns:**
+- Tuple of (label, similarity) for best match, or None if no match above threshold
+
+#### `detect_and_match()` - One-step detection and matching
+
+```python
+matches = detector.detect_and_match(
+    image,
+    reference_embeddings,
+    similarity_threshold=0.7
+)
+```
+
+Convenience method that combines detection and matching in one step.
+
+**Returns:**
+- List of matched detections, each containing:
+  - `box`: Bounding box coordinates
+  - `detr_score`: DETR confidence score
+  - `clip_similarity`: CLIP similarity score
+  - `label`: Matched reference logo label
+
+### 4. **Advanced Matching Methods**
+
+These methods provide improved accuracy over basic matching.
+
+#### `find_best_match_with_margin()` - Margin-based matching
+
+Requires the best match to exceed the second-best by a minimum margin, reducing false positives from ambiguous matches.
+
+```python
+match = detector.find_best_match_with_margin(
+    detected_embedding,
+    reference_embeddings,      # List of (label, embedding) tuples
+    similarity_threshold=0.85,
+    margin=0.05
+)
+# Returns: (label, similarity) or None
+```
+
+**Parameters:**
+- `detected_embedding`: CLIP embedding from detected logo region
+- `reference_embeddings`: List of (label, embedding) tuples for reference logos
+- `similarity_threshold`: Minimum similarity to consider a match (0-1, default: 0.85)
+- `margin`: Required difference between best and second-best match (default: 0.05)
+
+**Returns:**
+- Tuple of (label, similarity) for best match, or None if:
+  - No match above threshold, OR
+  - Best match doesn't exceed second-best by the required margin
+
+**Example:**
+```python
+# Best match: Logo A (0.82), Second best: Logo B (0.79)
+# With margin=0.05: No match returned (0.82 - 0.79 = 0.03 < 0.05)
+# This prevents false positives when multiple logos look similar
+```
+
+#### `find_best_match_multi_ref()` - Multi-reference matching
+
+Uses multiple reference images per logo for more robust matching, aggregating similarity scores across references.
+
+```python
+match = detector.find_best_match_multi_ref(
+    detected_embedding,
+    reference_embeddings,       # Dict: logo_name -> list of embeddings
+    similarity_threshold=0.85,
+    min_matching_refs=1,
+    use_mean_similarity=True
+)
+# Returns: (label, similarity, num_matching_refs) or None
+```
+
+**Parameters:**
+- `detected_embedding`: CLIP embedding from detected logo region
+- `reference_embeddings`: Dict mapping logo name to list of embeddings
+- `similarity_threshold`: Minimum similarity to consider a match (0-1, default: 0.85)
+- `min_matching_refs`: Minimum number of references that must match above threshold (default: 1)
+- `use_mean_similarity`: If True, use mean similarity; if False, use max (default: True)
+
+**Returns:**
+- Tuple of (label, similarity, num_matching_refs) for best match, or None if no match meets criteria
+
+**Example:**
+```python
+# Build multi-ref embeddings dict
+multi_ref_embeddings = {
+    "Nike": [embedding1, embedding2, embedding3],
+    "Adidas": [embedding4, embedding5],
+}
+
+match = detector.find_best_match_multi_ref(
+    detected_embedding,
+    multi_ref_embeddings,
+    similarity_threshold=0.80,
+    min_matching_refs=2,        # At least 2 refs must match
+    use_mean_similarity=True    # Average across all refs
+)
+
+if match:
+    label, avg_similarity, num_refs_matched = match
+    print(f"Matched {label} with {avg_similarity:.3f} ({num_refs_matched} refs matched)")
+```
+
+## Usage Pattern (Similar to Face Recognition)
+
+The class is designed to work with the caching pattern in scan.py:
+
+```python
+from scan_utils.logo_detection_detr import DetectLogosDETR
+
+# Initialize detector
+detector = DetectLogosDETR(logger, detr_threshold=0.5)
+
+# 1. Get embeddings for detected logos (cached per image)
+detections = detector.detect(target_image)
+
+# 2. Get/cache reference logo embeddings
+reference_embeddings = []
+for logo_file in reference_logos:
+    # Check cache first (kvstore)
+    logo_key = make_image_key("logo_reference", logo_file)
+    embedding = kv.get_torch(logo_key)
+
+    if embedding is None:
+        # Load and compute embedding
+        logo_img = image_processor.load_image_safely(logo_file)
+        embedding = detector.get_embedding(logo_img)
+
+        # Cache for future use
+        kv.put_torch(logo_key, embedding)
+
+    reference_embeddings.append((logo_name, embedding))
+
+# 3. Match detections against references
+matched_logos = []
+for detection in detections:
+    match = detector.find_best_match(
+        detection['embedding'],
+        reference_embeddings,
+        similarity_threshold=0.7
+    )
+
+    if match:
+        label, similarity = match
+        matched_logos.append({
+            'label': label,
+            'box': detection['box'],
+            'detr_score': detection['score'],
+            'clip_similarity': similarity
+        })
+        # Logo identified!
+```
+
+## Caching Strategy
+
+This follows the same caching pattern as facial recognition:
+
+1. **Target Image Embeddings**: Cache DETR detections and CLIP embeddings per image
+   - Key: `make_image_key("logo_detection", image_path)`
+   - Avoids re-running DETR on the same image
+
+2. **Reference Logo Embeddings**: Cache CLIP embeddings for reference logos
+   - Key: `make_image_key("logo_reference", logo_path)`
+   - Computed once and reused across all image scans
+
+3. **Benefits**:
+   - DETR only runs once per target image
+   - CLIP only runs once per reference logo
+   - Subsequent scans only perform embedding comparisons (very fast)
+
+## Integration Example
+
+```python
+def detect_logos_with_caching(
+    detector,
+    img_file,
+    reference_logos,
+    max_size=1920
+):
+    # Load and resize image
+    im_in = image_processor.load_image_safely(img_file)
+    img = resize_if_needed_opt(im_in, max_size)
+
+    # Check cache for detections
+    detection_key = make_image_key("logo_detection", img_file)
+    cached_data = kv.get(detection_key)
+
+    if cached_data:
+        # Use cached detections
+        detections = json.loads(cached_data)
+        logger.debug("Logo detections loaded from cache")
+    else:
+        # Run detection and cache results
+        detections = detector.detect(img)
+        kv.put(detection_key, json.dumps(detections))
+
+    # Load reference embeddings (with caching)
+    reference_embeddings = []
+    for logo_name, logo_path in reference_logos:
+        ref_key = make_image_key("logo_reference", logo_path)
+        embedding = kv.get_torch(ref_key)
+
+        if embedding is None:
+            logo_img = image_processor.load_image_safely(logo_path)
+            embedding = detector.get_embedding(logo_img)
+            kv.put_torch(ref_key, embedding)
+
+        reference_embeddings.append((logo_name, embedding))
+
+    # Match and return results
+    return detector.detect_and_match(
+        img,
+        reference_embeddings,
+        similarity_threshold=0.7
+    )
+```
+
+## Performance Considerations
+
+- **First Run**: Slower (DETR + CLIP inference)
+- **Cached Runs**: Much faster (only embedding comparisons)
+- **GPU Acceleration**: Automatically uses CUDA if available
+- **Memory**: Models loaded once and reused across all images
--- a/logo_detection_test_methodology.md
+++ b/logo_detection_test_methodology.md
@ -0,0 +1,308 @@
+# Logo Detection Test Methodology
+
+This document describes how the logo detection test framework works and the various techniques implemented to improve detection accuracy.
+
+## Overview
+
+The system uses a two-stage pipeline:
+1. **DETR** (DEtection TRansformer) - Detects potential logo regions in images
+2. **CLIP** (Contrastive Language-Image Pre-training) - Extracts feature embeddings for matching
+
+## Test Framework (`test_logo_detection.py`)
+
+### Test Flow
+
+1. **Sample Reference Logos**: Randomly select N logos from the database, with multiple reference images per logo
+2. **Compute Reference Embeddings**: Generate CLIP embeddings for all reference logo images
+3. **Build Test Set**: For each sampled logo, select:
+   - Positive samples: Images known to contain the logo
+   - Negative samples: Images known NOT to contain the logo
+4. **Run Detection**: Process each test image through DETR to find logo regions
+5. **Match Against References**: Compare detected regions against reference embeddings using margin-based matching
+6. **Calculate Metrics**: Compute precision, recall, and F1 score
+
+### Configurable Parameters
+
+#### General Parameters
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--num-logos` | 10 | Number of reference logos to sample |
+| `--refs-per-logo` | 3 | Reference images per logo |
+| `--positive-samples` | 5 | Positive test images per logo |
+| `--negative-samples` | 20 | Negative test images per logo |
+| `--threshold` | 0.7 | CLIP similarity threshold for matching |
+| `--detr-threshold` | 0.5 | DETR detection confidence threshold |
+| `--seed` | None | Random seed for reproducibility |
+
+#### Matching Method Selection
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--matching-method` | margin | Matching method: `margin` or `multi-ref` |
+
+#### Margin Method Parameters (when `--matching-method margin`)
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--margin` | 0.05 | Required margin between best and second-best match |
+
+#### Multi-Ref Method Parameters (when `--matching-method multi-ref`)
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--min-matching-refs` | 1 | Minimum references that must match above threshold |
+| `--use-max-similarity` | False | Use max similarity instead of mean across references |
+
+#### Cache Control
+
+| Parameter | Default | Description |
+|-----------|---------|-------------|
+| `--no-cache` | False | Disable embedding cache |
+| `--clear-cache` | False | Clear cache before running |
+
+### Metrics
+
+- **True Positives**: Detected logo correctly matches expected logo
+- **False Positives**: Detected logo matches wrong logo or image has no logo
+- **False Negatives**: Expected logo not detected/matched
+- **Precision**: TP / (TP + FP) - How many detections were correct
+- **Recall**: TP / Total Expected - How many logos were found
+- **F1 Score**: Harmonic mean of precision and recall
+
+---
+
+## Accuracy Improvement Techniques
+
+### 1. Non-Maximum Suppression (NMS)
+
+**Location**: `logo_detection_detr.py:214-268`
+
+**Problem**: DETR may produce multiple overlapping bounding boxes for the same logo.
+
+**Solution**: NMS removes redundant detections by:
+1. Sorting detections by confidence score (descending)
+2. Keeping the highest-scoring box
+3. Removing any remaining boxes with IoU > threshold (default 0.5)
+4. Repeating until no boxes remain
+
+```
+IoU (Intersection over Union) = Area of Overlap / Area of Union
+```
+
+**Configuration**: `nms_iou_threshold` parameter (default: 0.5)
+
+---
+
+### 2. Minimum Box Size Filtering
+
+**Location**: `logo_detection_detr.py:187-191`
+
+**Problem**: Very small detections are often noise or partial logo fragments.
+
+**Solution**: Filter out detections where width OR height is below a minimum threshold.
+
+**Configuration**: `min_box_size` parameter (default: 20 pixels)
+
+---
+
+### 3. Confidence Threshold Filtering
+
+**Location**: `logo_detection_detr.py:177-179`
+
+**Problem**: Low-confidence DETR detections are unreliable.
+
+**Solution**: Only keep detections with confidence score >= threshold.
+
+**Configuration**: `detr_threshold` parameter (default: 0.5)
+
+---
+
+### 4. Multiple Reference Images Per Logo
+
+**Location**: `logo_detection_detr.py:397-457` (`find_best_match_multi_ref`)
+
+**Problem**: A single reference image may not capture all variations of a logo (different angles, lighting, scales).
+
+**Solution**: Use multiple reference images per logo and aggregate their similarity scores:
+- Calculate similarity to each reference embedding
+- Count how many references match above threshold
+- Use mean or max similarity as the aggregate score
+- Require a minimum number of references to match
+
+**Configuration**:
+- `refs_per_logo`: Number of reference images (default: 3)
+- `min_matching_refs`: Minimum references that must match
+- `use_mean_similarity`: Use mean vs max aggregation
+
+---
+
+### 5. Margin-Based Matching
+
+**Location**: `logo_detection_detr.py:459-505` (`find_best_match_with_margin`)
+
+**Problem**: When multiple logos have similar embeddings, the best match may not be significantly better than alternatives, leading to false positives.
+
+**Solution**: Require the best match to exceed the second-best match by a minimum margin:
+
+```
+Match only if: best_similarity - second_best_similarity >= margin
+```
+
+This ensures confident matches and reduces ambiguous classifications.
+
+**Configuration**: `--margin` parameter (default: 0.05)
+
+**Example**:
+- Best match: Logo A with similarity 0.82
+- Second best: Logo B with similarity 0.79
+- Margin required: 0.05
+- Result: **No match** (0.82 - 0.79 = 0.03 < 0.05)
+
+---
+
+### 6. Embedding Caching
+
+**Location**: `test_logo_detection.py:49-82` (`EmbeddingCache` class)
+
+**Problem**: Computing CLIP embeddings is computationally expensive. Re-running tests would reprocess the same images.
+
+**Solution**: Cache embeddings to disk using pickle:
+- Reference embeddings keyed by `ref:{filename}`
+- Detection results keyed by `det:{filename}`
+- Cache persists between runs (`.embedding_cache.pkl`)
+
+**Configuration**:
+- `--no-cache`: Disable caching entirely
+- `--clear-cache`: Clear cache before running
+
+---
+
+### 7. Normalized Embeddings for Cosine Similarity
+
+**Location**: `logo_detection_detr.py:334-335`
+
+**Problem**: Raw CLIP embeddings have varying magnitudes, which can affect similarity calculations.
+
+**Solution**: L2-normalize all embeddings before comparison:
+
+```python
+features = F.normalize(features, dim=-1)
+```
+
+This ensures cosine similarity is computed correctly and scores fall in the range [-1, 1].
+
+---
+
+## Matching Methods Summary
+
+| Method | Test Script Option | Key Feature |
+|--------|-------------------|-------------|
+| `find_best_match` | N/A (library only) | Returns highest similarity above threshold |
+| `find_best_match_with_margin` | `--matching-method margin` | Requires margin over second-best match |
+| `find_best_match_multi_ref` | `--matching-method multi-ref` | Aggregates scores across reference images |
+
+The test script supports both `margin` and `multi-ref` matching methods via the `--matching-method` parameter.
+
+---
+
+## Detection Pipeline Summary
+
+```
+Input Image
+    │
+    ▼
+┌─────────────────────────────────────┐
+│  DETR Object Detection              │
+│  - Identifies potential logo regions│
+│  - Returns bounding boxes + scores  │
+└─────────────────────────────────────┘
+    │
+    ▼
+┌─────────────────────────────────────┐
+│  Confidence Filtering               │
+│  - Remove detections < threshold    │
+└─────────────────────────────────────┘
+    │
+    ▼
+┌─────────────────────────────────────┐
+│  Size Filtering                     │
+│  - Remove boxes < min_box_size      │
+└─────────────────────────────────────┘
+    │
+    ▼
+┌─────────────────────────────────────┐
+│  CLIP Embedding Extraction          │
+│  - Crop each detected region        │
+│  - Generate normalized embedding    │
+└─────────────────────────────────────┘
+    │
+    ▼
+┌─────────────────────────────────────┐
+│  Non-Maximum Suppression            │
+│  - Remove overlapping detections    │
+│  - Keep highest confidence boxes    │
+└─────────────────────────────────────┘
+    │
+    ▼
+┌─────────────────────────────────────┐
+│  Matching (selectable method)       │
+│  ┌───────────────┬────────────────┐ │
+│  │ margin        │ multi-ref      │ │
+│  ├───────────────┼────────────────┤ │
+│  │ Require margin│ Aggregate      │ │
+│  │ over 2nd best │ across refs    │ │
+│  │ match         │ (mean or max)  │ │
+│  └───────────────┴────────────────┘ │
+└─────────────────────────────────────┘
+    │
+    ▼
+Matched Logo Labels
+```
+
+---
+
+## Tuning Recommendations
+
+### For Margin-Based Matching (`--matching-method margin`)
+
+| Goal | Adjustments |
+|------|-------------|
+| **Reduce false positives** | Increase `--threshold`, increase `--margin` |
+| **Reduce false negatives** | Decrease `--threshold`, decrease `--margin` |
+
+### For Multi-Ref Matching (`--matching-method multi-ref`)
+
+| Goal | Adjustments |
+|------|-------------|
+| **Reduce false positives** | Increase `--threshold`, increase `--min-matching-refs`, use mean similarity |
+| **Reduce false negatives** | Decrease `--threshold`, decrease `--min-matching-refs`, use `--use-max-similarity` |
+
+### General Tuning
+
+| Goal | Adjustments |
+|------|-------------|
+| **Faster processing** | Decrease `--refs-per-logo`, use caching |
+| **More robust detection** | Increase `--refs-per-logo`, decrease `--detr-threshold` |
+| **Higher precision** | Increase `--detr-threshold`, use margin method with high margin |
+| **Higher recall** | Decrease `--detr-threshold`, use multi-ref with low `--min-matching-refs` |
+
+---
+
+## Example Usage
+
+```bash
+# Default margin-based matching
+python test_logo_detection.py -n 20 --threshold 0.75 --margin 0.05
+
+# Multi-ref matching with mean similarity
+python test_logo_detection.py -n 20 --matching-method multi-ref \
+    --refs-per-logo 5 --min-matching-refs 2 --threshold 0.70
+
+# Multi-ref matching with max similarity (more lenient)
+python test_logo_detection.py -n 20 --matching-method multi-ref \
+    --refs-per-logo 5 --min-matching-refs 1 --use-max-similarity
+
+# Reproducible test with seed
+python test_logo_detection.py -n 50 --seed 42 --clear-cache
+```
--- a/main.py
+++ b/main.py
@ -0,0 +1,6 @@
+def main():
+    print("Hello from logo-test!")
+
+
+if __name__ == "__main__":
+    main()
--- a/prepare_test_data.py
+++ b/prepare_test_data.py
@ -0,0 +1,322 @@
+#!/usr/bin/env python3
+"""
+Prepare test data from LogoDet-3K dataset.
+
+This script:
+1. Scans LogoDet-3K for images and XML annotation files
+2. Extracts cropped logos using bounding box data and saves to reference_logos/
+3. Copies full images to test_images/ with unique filenames
+4. Creates a SQLite database for storing mappings and verification
+"""
+
+import sqlite3
+import shutil
+import xml.etree.ElementTree as ET
+from pathlib import Path
+from PIL import Image
+from tqdm import tqdm
+
+
+def parse_xml_annotation(xml_path: Path) -> dict:
+    """Parse Pascal VOC format XML annotation file."""
+    tree = ET.parse(xml_path)
+    root = tree.getroot()
+
+    annotation = {
+        "filename": root.find("filename").text,
+        "size": {
+            "width": int(root.find("size/width").text),
+            "height": int(root.find("size/height").text),
+        },
+        "objects": []
+    }
+
+    for obj in root.findall("object"):
+        bbox = obj.find("bndbox")
+        annotation["objects"].append({
+            "name": obj.find("name").text,
+            "xmin": int(bbox.find("xmin").text),
+            "ymin": int(bbox.find("ymin").text),
+            "xmax": int(bbox.find("xmax").text),
+            "ymax": int(bbox.find("ymax").text),
+        })
+
+    return annotation
+
+
+def sanitize_filename(name: str) -> str:
+    """Convert logo name to a safe filename."""
+    # Replace problematic characters
+    safe = name.replace("/", "_").replace("\\", "_").replace(" ", "_")
+    safe = safe.replace(":", "_").replace("*", "_").replace("?", "_")
+    safe = safe.replace('"', "_").replace("<", "_").replace(">", "_")
+    safe = safe.replace("|", "_")
+    return safe
+
+
+def init_database(db_path: Path) -> sqlite3.Connection:
+    """Initialize SQLite database with schema."""
+    # Remove existing database if present
+    if db_path.exists():
+        db_path.unlink()
+
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+
+    # Create tables
+    cursor.executescript("""
+        -- Test images table
+        CREATE TABLE test_images (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            filename TEXT UNIQUE NOT NULL
+        );
+
+        -- Logo names table (unique brand/logo identifiers)
+        CREATE TABLE logo_names (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            name TEXT UNIQUE NOT NULL
+        );
+
+        -- Reference logos table with foreign keys
+        CREATE TABLE reference_logos (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            filename TEXT UNIQUE NOT NULL,
+            test_image_id INTEGER NOT NULL,
+            logo_name_id INTEGER NOT NULL,
+            FOREIGN KEY (test_image_id) REFERENCES test_images(id),
+            FOREIGN KEY (logo_name_id) REFERENCES logo_names(id)
+        );
+
+        -- Statistics table for metadata
+        CREATE TABLE statistics (
+            key TEXT PRIMARY KEY,
+            value INTEGER NOT NULL
+        );
+
+        -- Indexes for faster lookups
+        CREATE INDEX idx_reference_logos_test_image ON reference_logos(test_image_id);
+        CREATE INDEX idx_reference_logos_logo_name ON reference_logos(logo_name_id);
+    """)
+
+    conn.commit()
+    return conn
+
+
+def get_or_create_logo_name(cursor: sqlite3.Cursor, name: str) -> int:
+    """Get existing logo_name id or create new one."""
+    cursor.execute("SELECT id FROM logo_names WHERE name = ?", (name,))
+    row = cursor.fetchone()
+    if row:
+        return row[0]
+    cursor.execute("INSERT INTO logo_names (name) VALUES (?)", (name,))
+    return cursor.lastrowid
+
+
+def main():
+    # Paths
+    dataset_dir = Path("/data/dev.python/logo_test/LogoDet-3K")
+    reference_dir = Path("/data/dev.python/logo_test/reference_logos")
+    test_images_dir = Path("/data/dev.python/logo_test/test_images")
+    db_path = Path("/data/dev.python/logo_test/test_data_mapping.db")
+
+    # Ensure output directories exist
+    reference_dir.mkdir(exist_ok=True)
+    test_images_dir.mkdir(exist_ok=True)
+
+    # Initialize database
+    print(f"Initializing database at {db_path}...")
+    conn = init_database(db_path)
+    cursor = conn.cursor()
+
+    # Find all XML files
+    print("Scanning for XML annotation files...")
+    xml_files = list(dataset_dir.rglob("*.xml"))
+    print(f"Found {len(xml_files)} annotation files")
+
+    # Track unique filenames to avoid conflicts (keyed by subdirectory tuple)
+    used_test_filenames = {}
+    used_ref_filenames = {}
+
+    # Counters for progress
+    stats = {
+        "images_processed": 0,
+        "logos_extracted": 0,
+        "skipped_missing_image": 0,
+        "skipped_invalid_bbox": 0,
+    }
+
+    # Process each XML file
+    print("\nProcessing annotations...")
+    for xml_path in tqdm(xml_files, desc="Processing", unit="file"):
+        try:
+            annotation = parse_xml_annotation(xml_path)
+        except Exception as e:
+            tqdm.write(f"Error parsing {xml_path}: {e}")
+            continue
+
+        # Find corresponding image file
+        image_filename = annotation["filename"]
+        image_path = xml_path.parent / image_filename
+
+        if not image_path.exists():
+            # Try common extensions
+            for ext in [".jpg", ".jpeg", ".png", ".JPG", ".JPEG", ".PNG"]:
+                alt_path = xml_path.parent / (xml_path.stem + ext)
+                if alt_path.exists():
+                    image_path = alt_path
+                    break
+
+        if not image_path.exists():
+            stats["skipped_missing_image"] += 1
+            continue
+
+        # Generate unique test image filename
+        # Use category/brand/original_name format to avoid conflicts
+        rel_path = xml_path.relative_to(dataset_dir)
+        category = rel_path.parts[0] if len(rel_path.parts) > 0 else "unknown"
+        brand = rel_path.parts[1] if len(rel_path.parts) > 1 else "unknown"
+
+        safe_category = sanitize_filename(category)
+        safe_brand = sanitize_filename(brand)
+        base_name = image_path.stem
+        ext = image_path.suffix
+
+        # Create subdirectory structure: category/brand/
+        test_subdir = test_images_dir / safe_category / safe_brand
+        test_subdir.mkdir(parents=True, exist_ok=True)
+
+        test_basename = f"{base_name}{ext}"
+
+        # Handle duplicates within subdirectory
+        counter = 1
+        while test_basename in used_test_filenames.get((safe_category, safe_brand), set()):
+            test_basename = f"{base_name}_{counter}{ext}"
+            counter += 1
+        used_test_filenames.setdefault((safe_category, safe_brand), set()).add(test_basename)
+
+        # Store relative path from test_images_dir for database
+        test_filename = f"{safe_category}/{safe_brand}/{test_basename}"
+
+        # Copy full image to test_images
+        test_image_path = test_subdir / test_basename
+        shutil.copy2(image_path, test_image_path)
+        stats["images_processed"] += 1
+
+        # Insert test image into database
+        cursor.execute(
+            "INSERT INTO test_images (filename) VALUES (?)",
+            (test_filename,)
+        )
+        test_image_id = cursor.lastrowid
+
+        # Load image for cropping
+        try:
+            img = Image.open(image_path)
+        except Exception as e:
+            tqdm.write(f"Error loading {image_path}: {e}")
+            continue
+
+        img_width, img_height = img.size
+
+        # Process each object/logo in the image
+        for obj_idx, obj in enumerate(annotation["objects"]):
+            logo_name = obj["name"]
+            xmin, ymin = obj["xmin"], obj["ymin"]
+            xmax, ymax = obj["xmax"], obj["ymax"]
+
+            # Validate bounding box
+            if xmin >= xmax or ymin >= ymax:
+                stats["skipped_invalid_bbox"] += 1
+                continue
+
+            # Clamp to image bounds
+            xmin = max(0, min(xmin, img_width - 1))
+            ymin = max(0, min(ymin, img_height - 1))
+            xmax = max(1, min(xmax, img_width))
+            ymax = max(1, min(ymax, img_height))
+
+            if xmin >= xmax or ymin >= ymax:
+                stats["skipped_invalid_bbox"] += 1
+                continue
+
+            # Crop logo region
+            try:
+                logo_crop = img.crop((xmin, ymin, xmax, ymax))
+            except Exception as e:
+                tqdm.write(f"Error cropping {image_path}: {e}")
+                stats["skipped_invalid_bbox"] += 1
+                continue
+
+            # Generate reference logo filename with subdirectory structure: category/logo_name/
+            safe_logo_name = sanitize_filename(logo_name)
+            ref_subdir = reference_dir / safe_category / safe_logo_name
+            ref_subdir.mkdir(parents=True, exist_ok=True)
+
+            ref_basename = f"{base_name}_{obj_idx}.png"
+
+            # Handle duplicates within subdirectory
+            counter = 1
+            while ref_basename in used_ref_filenames.get((safe_category, safe_logo_name), set()):
+                ref_basename = f"{base_name}_{obj_idx}_{counter}.png"
+                counter += 1
+            used_ref_filenames.setdefault((safe_category, safe_logo_name), set()).add(ref_basename)
+
+            # Store relative path from reference_dir for database
+            ref_filename = f"{safe_category}/{safe_logo_name}/{ref_basename}"
+
+            # Save cropped logo
+            ref_path = ref_subdir / ref_basename
+            try:
+                logo_crop.save(ref_path, "PNG")
+            except Exception as e:
+                tqdm.write(f"Error saving {ref_path}: {e}")
+                continue
+
+            stats["logos_extracted"] += 1
+
+            # Get or create logo_name entry
+            logo_name_id = get_or_create_logo_name(cursor, logo_name)
+
+            # Insert reference logo into database
+            cursor.execute(
+                "INSERT INTO reference_logos (filename, test_image_id, logo_name_id) VALUES (?, ?, ?)",
+                (ref_filename, test_image_id, logo_name_id)
+            )
+
+    # Get unique logo names count
+    cursor.execute("SELECT COUNT(*) FROM logo_names")
+    unique_logo_names = cursor.fetchone()[0]
+
+    # Save statistics to database
+    statistics_data = [
+        ("total_test_images", stats["images_processed"]),
+        ("total_reference_logos", stats["logos_extracted"]),
+        ("unique_logo_names", unique_logo_names),
+        ("skipped_missing_image", stats["skipped_missing_image"]),
+        ("skipped_invalid_bbox", stats["skipped_invalid_bbox"]),
+    ]
+    cursor.executemany(
+        "INSERT INTO statistics (key, value) VALUES (?, ?)",
+        statistics_data
+    )
+
+    # Commit and close database
+    conn.commit()
+    conn.close()
+
+    # Print summary
+    print("\n" + "=" * 60)
+    print("SUMMARY")
+    print("=" * 60)
+    print(f"Test images created:     {stats['images_processed']:,}")
+    print(f"Reference logos created: {stats['logos_extracted']:,}")
+    print(f"Unique logo names:       {unique_logo_names:,}")
+    print(f"Skipped (missing image): {stats['skipped_missing_image']:,}")
+    print(f"Skipped (invalid bbox):  {stats['skipped_invalid_bbox']:,}")
+    print(f"\nDatabase saved to: {db_path}")
+    print(f"Reference logos:  {reference_dir}")
+    print(f"Test images:      {test_images_dir}")
+
+
+if __name__ == "__main__":
+    main()
--- a/pyproject.toml
+++ b/pyproject.toml
@ -0,0 +1,15 @@
+[project]
+name = "logo-test"
+version = "0.1.0"
+description = "Add your description here"
+readme = "README.md"
+requires-python = ">=3.12"
+dependencies = [
+    "numpy>=2.2.6",
+    "opencv-python>=4.12.0.88",
+    "pillow>=12.0.0",
+    "torch>=2.9.1",
+    "tqdm>=4.67.1",
+    "transformers>=4.57.3",
+    "typing>=3.10.0.0",
+]
--- a/requirements.txt
+++ b/requirements.txt
@ -0,0 +1,44 @@
+certifi==2025.11.12
+charset-normalizer==3.4.4
+filelock==3.20.1
+fsspec==2025.12.0
+hf-xet==1.2.0
+huggingface-hub==0.36.0
+idna==3.11
+jinja2==3.1.6
+markupsafe==3.0.3
+mpmath==1.3.0
+networkx==3.6.1
+numpy==2.2.6
+nvidia-cublas-cu12==12.8.4.1
+nvidia-cuda-cupti-cu12==12.8.90
+nvidia-cuda-nvrtc-cu12==12.8.93
+nvidia-cuda-runtime-cu12==12.8.90
+nvidia-cudnn-cu12==9.10.2.21
+nvidia-cufft-cu12==11.3.3.83
+nvidia-cufile-cu12==1.13.1.3
+nvidia-curand-cu12==10.3.9.90
+nvidia-cusolver-cu12==11.7.3.90
+nvidia-cusparse-cu12==12.5.8.93
+nvidia-cusparselt-cu12==0.7.1
+nvidia-nccl-cu12==2.27.5
+nvidia-nvjitlink-cu12==12.8.93
+nvidia-nvshmem-cu12==3.3.20
+nvidia-nvtx-cu12==12.8.90
+opencv-python==4.12.0.88
+packaging==25.0
+pillow==12.0.0
+pyyaml==6.0.3
+regex==2025.11.3
+requests==2.32.5
+safetensors==0.7.0
+setuptools==80.9.0
+sympy==1.14.0
+tokenizers==0.22.1
+torch==2.9.1
+tqdm==4.67.1
+transformers==4.57.3
+triton==3.5.1
+typing==3.10.0.0
+typing-extensions==4.15.0
+urllib3==2.6.2
--- a/test_cuda_support.py
+++ b/test_cuda_support.py
@ -0,0 +1,275 @@
+#!/usr/bin/env python3
+"""
+CUDA Support Test for Nvidia Jetson Hardware
+
+This script verifies that OpenCV and PyTorch are properly configured
+with CUDA support on Jetson devices.
+
+Usage:
+    python test_cuda_support.py
+
+Returns:
+    Exit code 0 if CUDA is properly configured
+    Exit code 1 if CUDA support is missing or misconfigured
+"""
+
+import sys
+import platform
+
+
+def print_section(title):
+    """Print a section header."""
+    print("\n" + "=" * 60)
+    print(f"  {title}")
+    print("=" * 60)
+
+
+def test_pytorch_cuda():
+    """Test PyTorch CUDA support."""
+    print_section("PyTorch CUDA Support")
+
+    try:
+        import torch
+        print(f"✓ PyTorch imported successfully")
+        print(f"  Version: {torch.__version__}")
+
+        # Check CUDA availability
+        cuda_available = torch.cuda.is_available()
+        print(f"\nCUDA Available: {'✓ YES' if cuda_available else '✗ NO'}")
+
+        if cuda_available:
+            print(f"  CUDA Version: {torch.version.cuda}")
+            print(f"  cuDNN Version: {torch.backends.cudnn.version()}")
+            print(f"  cuDNN Enabled: {torch.backends.cudnn.enabled}")
+
+            # Get device information
+            device_count = torch.cuda.device_count()
+            print(f"\n  GPU Devices: {device_count}")
+
+            for i in range(device_count):
+                props = torch.cuda.get_device_properties(i)
+                print(f"\n  Device {i}: {props.name}")
+                print(f"    Compute Capability: {props.major}.{props.minor}")
+                print(f"    Total Memory: {props.total_memory / 1024**3:.2f} GB")
+                print(f"    Multi-Processor Count: {props.multi_processor_count}")
+
+            # Test tensor operations
+            print("\n  Testing GPU tensor operations...")
+            try:
+                x = torch.randn(3, 3).cuda()
+                y = torch.randn(3, 3).cuda()
+                z = x @ y
+                print(f"  ✓ GPU tensor operations successful")
+
+                # Check current device
+                print(f"  Current Device: {torch.cuda.current_device()}")
+                print(f"  Device Name: {torch.cuda.get_device_name(0)}")
+
+            except Exception as e:
+                print(f"  ✗ GPU tensor operations failed: {e}")
+                return False
+        else:
+            print("\n  ⚠ PyTorch CUDA is NOT available")
+            print("  Possible reasons:")
+            print("    - PyTorch not built with CUDA support")
+            print("    - CUDA drivers not installed")
+            print("    - Incompatible CUDA version")
+            return False
+
+        return cuda_available
+
+    except ImportError as e:
+        print(f"✗ Failed to import PyTorch: {e}")
+        return False
+    except Exception as e:
+        print(f"✗ Error testing PyTorch: {e}")
+        return False
+
+
+def test_opencv_cuda():
+    """Test OpenCV CUDA support."""
+    print_section("OpenCV CUDA Support")
+
+    try:
+        import cv2
+        print(f"✓ OpenCV imported successfully")
+        print(f"  Version: {cv2.__version__}")
+
+        # Check build information
+        build_info = cv2.getBuildInformation()
+
+        # Parse build info for CUDA
+        cuda_enabled = "CUDA:" in build_info and "YES" in build_info.split("CUDA:")[1].split("\n")[0]
+
+        print(f"\nCUDA Support: {'✓ YES' if cuda_enabled else '✗ NO'}")
+
+        if cuda_enabled:
+            # Extract CUDA-related information from build info
+            lines = build_info.split('\n')
+            cuda_section = False
+
+            print("\n  CUDA Build Configuration:")
+            for line in lines:
+                if 'CUDA' in line or cuda_section:
+                    if 'CUDA' in line:
+                        cuda_section = True
+                    if cuda_section:
+                        # Print relevant CUDA lines
+                        if any(keyword in line for keyword in ['CUDA', 'cuDNN', 'NVIDIA', 'GPU']):
+                            print(f"    {line.strip()}")
+                        # Stop at next major section
+                        if line.strip() and not line.startswith(' ') and 'CUDA' not in line:
+                            break
+
+            # Check for CUDA device count
+            try:
+                cuda_device_count = cv2.cuda.getCudaEnabledDeviceCount()
+                print(f"\n  CUDA Devices: {cuda_device_count}")
+
+                if cuda_device_count > 0:
+                    # Test CUDA operations
+                    print("\n  Testing CUDA operations...")
+                    try:
+                        # Create a simple GPU matrix
+                        import numpy as np
+                        test_img = np.random.randint(0, 255, (100, 100, 3), dtype=np.uint8)
+                        gpu_mat = cv2.cuda_GpuMat()
+                        gpu_mat.upload(test_img)
+                        result = gpu_mat.download()
+                        print(f"  ✓ CUDA operations successful")
+                    except Exception as e:
+                        print(f"  ✗ CUDA operations failed: {e}")
+                        return False
+                else:
+                    print("  ⚠ No CUDA devices detected")
+                    return False
+
+            except AttributeError:
+                print("  ⚠ cv2.cuda module not available")
+                print("  OpenCV may not be built with CUDA support")
+                return False
+        else:
+            print("\n  ⚠ OpenCV CUDA is NOT available")
+            print("  Possible reasons:")
+            print("    - OpenCV not built with CUDA support")
+            print("    - Need to install opencv-contrib-python with CUDA")
+            print("    - For Jetson, may need to build from source")
+            return False
+
+        return cuda_enabled
+
+    except ImportError as e:
+        print(f"✗ Failed to import OpenCV: {e}")
+        return False
+    except Exception as e:
+        print(f"✗ Error testing OpenCV: {e}")
+        return False
+
+
+def print_system_info():
+    """Print system information."""
+    print_section("System Information")
+
+    print(f"Platform: {platform.platform()}")
+    print(f"Python Version: {platform.python_version()}")
+    print(f"Architecture: {platform.machine()}")
+    print(f"Processor: {platform.processor()}")
+
+    # Try to detect Jetson
+    try:
+        with open('/etc/nv_tegra_release', 'r') as f:
+            jetson_version = f.read().strip()
+            print(f"\n✓ Jetson Device Detected")
+            print(f"  {jetson_version}")
+    except FileNotFoundError:
+        print("\n  Not running on Jetson device (or /etc/nv_tegra_release not found)")
+
+    # Check for CUDA toolkit
+    import subprocess
+    try:
+        result = subprocess.run(['nvcc', '--version'],
+                              capture_output=True,
+                              text=True,
+                              timeout=5)
+        if result.returncode == 0:
+            print("\n✓ CUDA Toolkit detected:")
+            # Print version line
+            for line in result.stdout.split('\n'):
+                if 'release' in line.lower():
+                    print(f"  {line.strip()}")
+        else:
+            print("\n⚠ nvcc not found - CUDA toolkit may not be installed")
+    except FileNotFoundError:
+        print("\n⚠ nvcc not found - CUDA toolkit may not be installed")
+    except Exception as e:
+        print(f"\n⚠ Error checking CUDA toolkit: {e}")
+
+
+def print_memory_info():
+    """Print GPU memory information."""
+    print_section("GPU Memory Information")
+
+    try:
+        import torch
+        if torch.cuda.is_available():
+            device = torch.cuda.current_device()
+            total_mem = torch.cuda.get_device_properties(device).total_memory
+            allocated = torch.cuda.memory_allocated(device)
+            cached = torch.cuda.memory_reserved(device)
+
+            print(f"Total GPU Memory: {total_mem / 1024**3:.2f} GB")
+            print(f"Allocated: {allocated / 1024**3:.2f} GB")
+            print(f"Cached: {cached / 1024**3:.2f} GB")
+            print(f"Free: {(total_mem - allocated) / 1024**3:.2f} GB")
+        else:
+            print("CUDA not available - cannot query GPU memory")
+    except Exception as e:
+        print(f"Error querying GPU memory: {e}")
+
+
+def main():
+    """Main test function."""
+    print("\n" + "=" * 60)
+    print("  CUDA Support Verification for Nvidia Jetson")
+    print("=" * 60)
+
+    # Print system info
+    print_system_info()
+
+    # Test PyTorch
+    pytorch_cuda = test_pytorch_cuda()
+
+    # Test OpenCV
+    opencv_cuda = test_opencv_cuda()
+
+    # Print memory info
+    if pytorch_cuda:
+        print_memory_info()
+
+    # Print summary
+    print_section("Summary")
+
+    print(f"PyTorch CUDA Support: {'✓ ENABLED' if pytorch_cuda else '✗ DISABLED'}")
+    print(f"OpenCV CUDA Support:  {'✓ ENABLED' if opencv_cuda else '✗ DISABLED'}")
+
+    if pytorch_cuda and opencv_cuda:
+        print("\n✓ All CUDA checks passed - system ready for GPU-accelerated processing")
+        return 0
+    elif pytorch_cuda:
+        print("\n⚠ PyTorch CUDA enabled, but OpenCV CUDA disabled")
+        print("  Some operations will use GPU, but OpenCV operations will use CPU")
+        return 1
+    elif opencv_cuda:
+        print("\n⚠ OpenCV CUDA enabled, but PyTorch CUDA disabled")
+        print("  OpenCV operations will use GPU, but PyTorch models will use CPU")
+        return 1
+    else:
+        print("\n✗ CUDA support not available - will run in CPU mode")
+        print("  Performance will be significantly slower")
+        return 1
+
+
+if __name__ == "__main__":
+    exit_code = main()
+    print("\n" + "=" * 60 + "\n")
+    sys.exit(exit_code)
--- a/test_logo_detection.py
+++ b/test_logo_detection.py
@ -0,0 +1,553 @@
+#!/usr/bin/env python3
+"""
+Test script for logo detection accuracy.
+
+This script:
+1. Randomly samples N reference logos from the database
+2. Processes all test images through the DETR+CLIP pipeline
+3. Compares detected logos against reference embeddings
+4. Reports accuracy metrics (correct matches, false positives, missed detections)
+
+Embeddings are cached to avoid reprocessing images.
+"""
+
+import argparse
+import logging
+import pickle
+import random
+import sqlite3
+import sys
+from pathlib import Path
+from typing import Dict, List, Optional, Set, Tuple
+
+import cv2
+import torch
+from tqdm import tqdm
+
+from logo_detection_detr import DetectLogosDETR
+
+
+def setup_logging(verbose: bool = False) -> logging.Logger:
+    """Configure logging."""
+    level = logging.DEBUG if verbose else logging.INFO
+    logging.basicConfig(
+        level=level,
+        format="%(asctime)s - %(levelname)s - %(message)s",
+        datefmt="%H:%M:%S",
+    )
+    return logging.getLogger(__name__)
+
+
+def load_image(image_path: Path) -> Optional[cv2.Mat]:
+    """Load an image using OpenCV."""
+    img = cv2.imread(str(image_path))
+    if img is None:
+        return None
+    return img
+
+
+class EmbeddingCache:
+    """Simple file-based cache for embeddings."""
+
+    def __init__(self, cache_path: Path):
+        self.cache_path = cache_path
+        self.cache: Dict[str, torch.Tensor] = {}
+        self._load()
+
+    def _load(self):
+        """Load cache from disk if it exists."""
+        if self.cache_path.exists():
+            try:
+                with open(self.cache_path, "rb") as f:
+                    self.cache = pickle.load(f)
+            except Exception:
+                self.cache = {}
+
+    def save(self):
+        """Save cache to disk."""
+        self.cache_path.parent.mkdir(parents=True, exist_ok=True)
+        with open(self.cache_path, "wb") as f:
+            pickle.dump(self.cache, f)
+
+    def get(self, key: str) -> Optional[torch.Tensor]:
+        """Get embedding from cache."""
+        return self.cache.get(key)
+
+    def put(self, key: str, embedding: torch.Tensor):
+        """Store embedding in cache."""
+        # Store on CPU to save GPU memory
+        self.cache[key] = embedding.cpu()
+
+    def __len__(self):
+        return len(self.cache)
+
+
+def get_ground_truth(db_path: Path) -> Tuple[Dict[str, Set[str]], Dict[str, Set[str]]]:
+    """
+    Load ground truth from database.
+
+    Returns:
+        Tuple of:
+        - Dict mapping test image filename to set of logo names it contains
+        - Dict mapping logo name to set of test image filenames containing it
+    """
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+
+    # Query to get test image -> logo names mapping
+    cursor.execute("""
+        SELECT ti.filename, ln.name
+        FROM test_images ti
+        JOIN reference_logos rl ON ti.id = rl.test_image_id
+        JOIN logo_names ln ON rl.logo_name_id = ln.id
+    """)
+
+    image_to_logos: Dict[str, Set[str]] = {}
+    logo_to_images: Dict[str, Set[str]] = {}
+
+    for row in cursor.fetchall():
+        test_filename, logo_name = row
+        if test_filename not in image_to_logos:
+            image_to_logos[test_filename] = set()
+        image_to_logos[test_filename].add(logo_name)
+
+        if logo_name not in logo_to_images:
+            logo_to_images[logo_name] = set()
+        logo_to_images[logo_name].add(test_filename)
+
+    conn.close()
+    return image_to_logos, logo_to_images
+
+
+def sample_reference_logos(
+    db_path: Path, num_logos: int, refs_per_logo: int = 1, seed: Optional[int] = None
+) -> Dict[str, List[str]]:
+    """
+    Randomly sample reference logos from database with multiple refs per logo.
+
+    Args:
+        db_path: Path to database
+        num_logos: Number of logos to sample
+        refs_per_logo: Number of reference images per logo
+        seed: Random seed for reproducibility
+
+    Returns:
+        Dict mapping logo_name to list of reference filenames
+    """
+    if seed is not None:
+        random.seed(seed)
+
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+
+    # Get all unique logo names
+    cursor.execute("SELECT id, name FROM logo_names")
+    all_logo_names = cursor.fetchall()
+
+    # Sample logos
+    if num_logos >= len(all_logo_names):
+        sampled_logos = all_logo_names
+    else:
+        sampled_logos = random.sample(all_logo_names, num_logos)
+
+    # For each sampled logo, get multiple reference files
+    result: Dict[str, List[str]] = {}
+    for logo_id, logo_name in sampled_logos:
+        cursor.execute(
+            "SELECT filename FROM reference_logos WHERE logo_name_id = ?",
+            (logo_id,)
+        )
+        all_refs = [row[0] for row in cursor.fetchall()]
+
+        # Sample refs_per_logo references (or all if fewer available)
+        if len(all_refs) > refs_per_logo:
+            selected_refs = random.sample(all_refs, refs_per_logo)
+        else:
+            selected_refs = all_refs
+
+        result[logo_name] = selected_refs
+
+    conn.close()
+    return result
+
+
+def get_test_images(db_path: Path) -> List[str]:
+    """Get all test image filenames from database."""
+    conn = sqlite3.connect(db_path)
+    cursor = conn.cursor()
+    cursor.execute("SELECT filename FROM test_images")
+    filenames = [row[0] for row in cursor.fetchall()]
+    conn.close()
+    return filenames
+
+
+def main():
+    parser = argparse.ArgumentParser(
+        description="Test logo detection accuracy against ground truth"
+    )
+    parser.add_argument(
+        "-n", "--num-logos",
+        type=int,
+        default=10,
+        help="Number of reference logos to sample (default: 10)",
+    )
+    parser.add_argument(
+        "-t", "--threshold",
+        type=float,
+        default=0.7,
+        help="CLIP similarity threshold for matching (default: 0.7)",
+    )
+    parser.add_argument(
+        "-d", "--detr-threshold",
+        type=float,
+        default=0.5,
+        help="DETR detection confidence threshold (default: 0.5)",
+    )
+    parser.add_argument(
+        "-s", "--seed",
+        type=int,
+        default=None,
+        help="Random seed for reproducibility",
+    )
+    parser.add_argument(
+        "--positive-samples",
+        type=int,
+        default=5,
+        help="Number of positive test images per logo (images containing the logo) (default: 5)",
+    )
+    parser.add_argument(
+        "--negative-samples",
+        type=int,
+        default=20,
+        help="Number of negative test images per logo (images NOT containing the logo) (default: 20)",
+    )
+    parser.add_argument(
+        "--refs-per-logo",
+        type=int,
+        default=3,
+        help="Number of reference images per logo for multi-ref matching (default: 3)",
+    )
+    parser.add_argument(
+        "--margin",
+        type=float,
+        default=0.05,
+        help="Required margin between best and second-best match for 'margin' method (default: 0.05)",
+    )
+    parser.add_argument(
+        "--matching-method",
+        type=str,
+        choices=["margin", "multi-ref"],
+        default="margin",
+        help="Matching method: 'margin' requires confidence margin over 2nd best, "
+             "'multi-ref' aggregates scores across reference images (default: margin)",
+    )
+    parser.add_argument(
+        "--min-matching-refs",
+        type=int,
+        default=1,
+        help="For 'multi-ref' method: minimum references that must match above threshold (default: 1)",
+    )
+    parser.add_argument(
+        "--use-max-similarity",
+        action="store_true",
+        help="For 'multi-ref' method: use max similarity instead of mean across references",
+    )
+    parser.add_argument(
+        "-v", "--verbose",
+        action="store_true",
+        help="Enable verbose logging",
+    )
+    parser.add_argument(
+        "--no-cache",
+        action="store_true",
+        help="Disable embedding cache",
+    )
+    parser.add_argument(
+        "--clear-cache",
+        action="store_true",
+        help="Clear embedding cache before running",
+    )
+
+    args = parser.parse_args()
+    logger = setup_logging(args.verbose)
+
+    # Paths
+    base_dir = Path(__file__).resolve().parent
+    db_path = base_dir / "test_data_mapping.db"
+    reference_dir = base_dir / "reference_logos"
+    test_images_dir = base_dir / "test_images"
+    cache_path = base_dir / ".embedding_cache.pkl"
+
+    # Verify database exists
+    if not db_path.exists():
+        logger.error(f"Database not found: {db_path}")
+        logger.error("Run prepare_test_data.py first to create the database.")
+        sys.exit(1)
+
+    # Handle cache clearing
+    if args.clear_cache and cache_path.exists():
+        cache_path.unlink()
+        logger.info("Cleared embedding cache")
+
+    # Initialize embedding cache
+    cache = EmbeddingCache(cache_path) if not args.no_cache else None
+    if cache:
+        logger.info(f"Loaded {len(cache)} cached embeddings")
+
+    # Initialize detector
+    logger.info("Initializing logo detector...")
+    detector = DetectLogosDETR(
+        logger=logger,
+        detr_threshold=args.detr_threshold,
+    )
+
+    # Load ground truth (both mappings)
+    logger.info("Loading ground truth from database...")
+    image_to_logos, logo_to_images = get_ground_truth(db_path)
+    all_test_images = set(image_to_logos.keys())
+    logger.info(f"Loaded ground truth for {len(image_to_logos)} test images")
+
+    # Sample reference logos (with multiple refs per logo)
+    logger.info(f"Sampling {args.num_logos} reference logos with {args.refs_per_logo} refs each...")
+    sampled_logos = sample_reference_logos(db_path, args.num_logos, args.refs_per_logo, args.seed)
+    logger.info(f"Selected {len(sampled_logos)} reference logos")
+
+    # Compute reference embeddings (multiple per logo for multi-ref matching)
+    logger.info("Computing reference logo embeddings...")
+    # Dict for multi-ref matching: logo_name -> list of embeddings
+    multi_ref_embeddings: Dict[str, List[torch.Tensor]] = {}
+    # List for margin-based matching: (logo_name, embedding) tuples
+    reference_embeddings: List[Tuple[str, torch.Tensor]] = []
+    total_refs = 0
+
+    for logo_name, ref_filenames in tqdm(sampled_logos.items(), desc="Reference logos"):
+        multi_ref_embeddings[logo_name] = []
+
+        for ref_filename in ref_filenames:
+            ref_path = reference_dir / ref_filename
+
+            if not ref_path.exists():
+                logger.warning(f"Reference logo not found: {ref_path}")
+                continue
+
+            # Check cache
+            cache_key = f"ref:{ref_filename}"
+            embedding = cache.get(cache_key) if cache else None
+
+            if embedding is None:
+                img = load_image(ref_path)
+                if img is None:
+                    logger.warning(f"Failed to load reference logo: {ref_path}")
+                    continue
+                embedding = detector.get_embedding(img)
+                if cache:
+                    cache.put(cache_key, embedding)
+
+            multi_ref_embeddings[logo_name].append(embedding)
+            reference_embeddings.append((logo_name, embedding))
+            total_refs += 1
+
+    logger.info(f"Computed {total_refs} embeddings for {len(sampled_logos)} logos")
+
+    # Build test set: for each logo, sample positive and negative images
+    logger.info(f"Sampling test images: {args.positive_samples} positive, {args.negative_samples} negative per logo...")
+    test_image_set: Set[str] = set()
+    test_image_expected: Dict[str, Set[str]] = {}  # image -> logos it should match
+
+    # Use sampled_logos keys (unique logo names) instead of reference_embeddings
+    for logo_name in sampled_logos.keys():
+        # Get positive images (contain this logo)
+        positive_images = list(logo_to_images.get(logo_name, set()))
+        if len(positive_images) > args.positive_samples:
+            positive_images = random.sample(positive_images, args.positive_samples)
+
+        # Get negative images (do NOT contain this logo)
+        negative_pool = list(all_test_images - logo_to_images.get(logo_name, set()))
+        if len(negative_pool) > args.negative_samples:
+            negative_images = random.sample(negative_pool, args.negative_samples)
+        else:
+            negative_images = negative_pool
+
+        # Add to test set
+        for img in positive_images:
+            test_image_set.add(img)
+            if img not in test_image_expected:
+                test_image_expected[img] = set()
+            test_image_expected[img].add(logo_name)
+
+        for img in negative_images:
+            test_image_set.add(img)
+            if img not in test_image_expected:
+                test_image_expected[img] = set()
+            # Don't add logo_name - this is a negative sample
+
+    test_images = list(test_image_set)
+    logger.info(f"Selected {len(test_images)} unique test images")
+
+    # Get set of reference logo names for quick lookup
+    reference_logo_names = set(sampled_logos.keys())
+
+    # Metrics
+    true_positives = 0  # Correctly matched logos
+    false_positives = 0  # Matched but wrong logo or no logo present
+    false_negatives = 0  # Logo present but not detected/matched
+    total_expected = 0  # Total logos we should have found
+
+    # Detailed results for analysis
+    results = []
+
+    # Process test images
+    for test_filename in tqdm(test_images, desc="Testing"):
+        test_path = test_images_dir / test_filename
+
+        if not test_path.exists():
+            logger.warning(f"Test image not found: {test_path}")
+            continue
+
+        # Get expected logos for this image (from our sampled set)
+        expected_logos = test_image_expected.get(test_filename, set())
+        total_expected += len(expected_logos)
+
+        # Check cache for detections
+        cache_key = f"det:{test_filename}"
+        cached_detections = cache.get(cache_key) if cache else None
+
+        if cached_detections is not None:
+            # Cached detections contain serialized box data and embeddings
+            detections = cached_detections
+        else:
+            # Load and detect
+            img = load_image(test_path)
+            if img is None:
+                logger.warning(f"Failed to load test image: {test_path}")
+                continue
+
+            detections = detector.detect(img)
+
+            # Cache the detections
+            if cache:
+                cache.put(cache_key, detections)
+
+        # Match detections against references using selected method
+        matched_logos: Set[str] = set()
+        for detection in detections:
+            match = None
+            similarity = None
+
+            if args.matching_method == "margin":
+                # Margin-based matching: requires margin over second-best
+                match_result = detector.find_best_match_with_margin(
+                    detection["embedding"],
+                    reference_embeddings,
+                    similarity_threshold=args.threshold,
+                    margin=args.margin,
+                )
+                if match_result:
+                    label, similarity = match_result
+                    match = label
+            else:  # multi-ref
+                # Multi-ref matching: aggregates scores across reference images
+                match_result = detector.find_best_match_multi_ref(
+                    detection["embedding"],
+                    multi_ref_embeddings,
+                    similarity_threshold=args.threshold,
+                    min_matching_refs=args.min_matching_refs,
+                    use_mean_similarity=not args.use_max_similarity,
+                )
+                if match_result:
+                    label, similarity, num_matching = match_result
+                    match = label
+
+            if match:
+                matched_logos.add(match)
+
+                # Check if this is a correct match
+                if match in expected_logos:
+                    true_positives += 1
+                else:
+                    false_positives += 1
+
+                results.append({
+                    "test_image": test_filename,
+                    "matched_logo": match,
+                    "similarity": similarity,
+                    "correct": match in expected_logos,
+                })
+
+        # Count missed detections (false negatives)
+        missed = expected_logos - matched_logos
+        false_negatives += len(missed)
+
+        for missed_logo in missed:
+            results.append({
+                "test_image": test_filename,
+                "matched_logo": None,
+                "expected_logo": missed_logo,
+                "similarity": None,
+                "correct": False,
+            })
+
+    # Save cache
+    if cache:
+        cache.save()
+        logger.info(f"Saved {len(cache)} embeddings to cache")
+
+    # Calculate metrics
+    precision = true_positives / (true_positives + false_positives) if (true_positives + false_positives) > 0 else 0
+    recall = true_positives / total_expected if total_expected > 0 else 0
+    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0
+
+    # Print results
+    print("\n" + "=" * 60)
+    print("LOGO DETECTION TEST RESULTS")
+    print("=" * 60)
+    print(f"\nConfiguration:")
+    print(f"  Reference logos sampled:   {len(sampled_logos)}")
+    print(f"  Refs per logo:             {args.refs_per_logo}")
+    print(f"  Total reference embeddings:{total_refs}")
+    print(f"  Positive samples per logo: {args.positive_samples}")
+    print(f"  Negative samples per logo: {args.negative_samples}")
+    print(f"  Test images processed:     {len(test_images)}")
+    print(f"  CLIP similarity threshold: {args.threshold}")
+    print(f"  DETR confidence threshold: {args.detr_threshold}")
+    print(f"  Matching method:           {args.matching_method}")
+    if args.matching_method == "margin":
+        print(f"  Matching margin:           {args.margin}")
+    else:  # multi-ref
+        print(f"  Min matching refs:         {args.min_matching_refs}")
+        print(f"  Similarity aggregation:    {'max' if args.use_max_similarity else 'mean'}")
+    if args.seed is not None:
+        print(f"  Random seed:               {args.seed}")
+
+    print(f"\nMetrics:")
+    print(f"  True Positives (correct matches):  {true_positives}")
+    print(f"  False Positives (wrong matches):   {false_positives}")
+    print(f"  False Negatives (missed logos):    {false_negatives}")
+    print(f"  Total expected matches:            {total_expected}")
+
+    print(f"\nScores:")
+    print(f"  Precision: {precision:.4f} ({precision*100:.1f}%)")
+    print(f"  Recall:    {recall:.4f} ({recall*100:.1f}%)")
+    print(f"  F1 Score:  {f1:.4f} ({f1*100:.1f}%)")
+
+    # Show some example false positives
+    false_positive_examples = [r for r in results if r.get("matched_logo") and not r["correct"]]
+    if false_positive_examples:
+        print(f"\nExample False Positives (first 5):")
+        for r in false_positive_examples[:5]:
+            print(f"  - Image: {r['test_image']}")
+            print(f"    Matched: {r['matched_logo']} (similarity: {r['similarity']:.3f})")
+
+    # Show reference logos used (unique names)
+    unique_logos = sorted(sampled_logos.keys())
+    print(f"\nReference logos used ({len(unique_logos)}):")
+    for name in unique_logos[:20]:
+        print(f"  - {name}")
+    if len(unique_logos) > 20:
+        print(f"  ... and {len(unique_logos) - 20} more")
+
+    print("=" * 60)
+
+
+if __name__ == "__main__":
+    main()
--- a/uv.lock
+++ b/uv.lock
@ -0,0 +1,869 @@
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