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🧠 Semantic Matching for Task-Specific Object Detection 🚀

Overview

This project is a Semester 5 Mini Project that combines cutting-edge techniques in Natural Language Processing (NLP) and Computer Vision to identify and select the best object for a user-defined task. It integrates models like BLIP, LLM (Large Language Models), and Vector Embedding Models to provide an end-to-end intelligent system.

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How It Works 🌟

Pipeline Diagram 📊

The project consists of two main flows:

1. Task Understanding

   • Takes user input, processes the task using an LLM, and generates a feature description vector.

2. Image Object Processing

   • Processes an input image to detect and describe objects using:
     • Object Detection Model for cropping objects.
     • BLIP (Bootstrapped Language-Image Pretraining) for text descriptions.
     • Embedding models to transform object descriptions into vectors.

3. Matching & Output

   • Combines the task and object vectors to find the best matching object for the task.

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Key Features ✨

• Multi-Modal Processing: Combines text and image understanding.
• LLM Integration: Transforms user-defined tasks into actionable features.
• BLIP for Vision-Language Tasks: Extracts meaningful text descriptions of objects.
• Vector Embedding Models: Ensures precise semantic matching.
• Efficient Object Matching: Identifies the best-suited object for any given task.

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Use Cases 🌍

• Robotics: Task-specific object selection for automated systems.
• Assistive Technology: Helping visually impaired users identify objects for tasks.
• Retail Search Engines: Matching customer queries to products.
• Content Analysis: Semantic understanding of objects in images.

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Tech Stack 🛠️

• Python: Core programming language.
• TensorFlow / PyTorch: For deep learning models.
• Sentence Transformers: Embedding generation for task and object descriptions.
• BLIP: For image-to-text processing.
• YOLO / Faster R-CNN: For object detection (depending on your choice).
• NumPy & Pandas: Data processing and analysis.

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Getting Started 🚀

Installation

1. Clone the repository:

   git clone https://github.com/username/project-name.git
   cd project-name

2. Install dependencies:

   pip install -r requirements.txt

3. Download the required pre-trained models:

   • BLIP: Download here
   • Sentence Transformers: Integrated via sentence_transformers package.

Folder Structure

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├── data/
│   ├── images/             # Input images
│   └── objects/            # Cropped objects from detection
├── models/
│   ├── object_detection/   # Object detection models
│   ├── BLIP/               # BLIP pre-trained weights
│   └── embeddings/         # Vector embedding models
├── src/
│   ├── preprocess.py       # Image preprocessing scripts
│   ├── task_vector.py      # Feature description vector generator
│   ├── match.py            # Combine and match vectors
│   └── utils.py            # Helper functions
└── README.md

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Running the Project

1. Add your image input file to the data/images folder.
2. Run the pipeline:

   python main.py --task "Pick up a cup" --image "data/images/sample.jpg"

3. View the best-matching object and its details in the console output.

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Example Output 🔥

Input Task:

"Pick up a cup"

Detected Objects:

• Bottle
• Cup
• Pen

Best Match:

• Cup 🥤

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Future Enhancements 🚀

• Real-Time Video Input: Extend the project to work with live video feeds.
• Interactive UI: Create a web or desktop app for user interaction.
• Domain-Specific Fine-Tuning: Customize models for robotics or healthcare.

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License 📜

This project is licensed under the MIT License - see the LICENSE file for details.