devranx/Pixel-Prompt-Annotator

✨ Annotation Assistant


Overview

Annotation Assistant is a state-of-the-art Vision-Language Object Detection tool. It combines the power of Qwen-VL (4B) with a user-friendly interface to make labeled data creation effortless.

Unlike standard detection tools, this assistant is conversational. You can refine detections naturally (e.g., "Also find the cup"), and the AI intelligently merges new findings with existing ones.

Key Features

🧠 Intelligent Memory & Context

The Assistant remembers what it has already found.

• —No Amnesia: Unlike basic wrappers, this tool feeds its own previous detections back into the context.
• —Example: If you say "Find the laptop" and then "Find the remaining objects", it understands what "remaining" means because it knows the laptop is already detected.

🎯 Smart Refinement Logic

I implemented a custom Weighted Merge Algorithm to handle updates:

• —Refinement: If you draw a better box for "shirt" over an existing one (>80% overlap), it replaces the old one.
• —Distinct Objects: If you seek a second "shirt" elsewhere (low overlap), it adds it as a new object.
• —Result: NO duplicate ghost boxes, NO accidental deletions.

👁️ Explainable AI (Reasoning)

Don't just trust the box. The Assistant provides a Reasoning Stream explaining why it detected an object.

• —Example: "Detected silver laptop due to distinct Apple logo and metallic finish."

How to Run

☁️ Option 1: Google Colab (Recommended for Free GPU)

1. 1.Open the Colab_Runner.ipynb file in Google Colab.
2. 2.Upload app.py, utils.py, and requirements.txt to the Colab files area.
3. 3.Add your Ngrok Authtoken in the designated cell.
4. 4.Run all cells. The app will launch via a public URL.

🤗 Option 2: Hugging Face Spaces (CPU/GPU)

1. 1.Create a new Space on Hugging Face.
2. 2.Select Streamlit as the SDK.
3. 3.Upload the files from this repository.
4. 4.The app will build and launch automatically.

💻 Option 3: Local System (Requires GPU)

1. 1.Clone the Repo:

    git clone https://github.com/devsingh02/Pixel-Prompt-Annotator.git
    cd Pixel-Prompt-Annotator

1. 1.Install Dependencies:

    pip install -r requirements.txt

1. 1.Run the App:

    streamlit run app.py

Built with Streamlit, Qwen-VL, and ❤️.