logo_test/CLAUDE.md

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

# 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.