Revise README with new project details and instructions

Updated project title and added detailed overview, features, installation instructions, and usage examples.

README.md

@@ -1,4 +1,91 @@
 # TextEraser
-Text-Guided Precise Object Removal with YOLOv8 + Stable Diffusion Inpainting
 
-Final Project for COMPSCI372: Intro to Applied Machine Learning @ Duke (25 Fall)
+**Text-Guided Object Removal using SAM2 + CLIP + Stable Diffusion XL**
+
+Final Project for COMPSCI372: Intro to Applied Machine Learning @ Duke University (Fall 2025)
+
+---
+
+## Overview
+
+TextEraser intelligently removes objects from images using natural language descriptions. Simply type what you want to remove (e.g., "bottle", "person", "car"), and the AI pipeline handles the rest.
+
+### Key Features
+
+- **Natural language control** - Remove objects by describing them in plain text
+- **Smart segmentation** - Uses SAM2 to find all objects in the image
+- **Intelligent matching** - CLIP identifies which segments match your description
+- **Seamless inpainting** - Stable Diffusion XL fills in the removed area naturally
+- **Multi-part object handling** - Automatically merges related segments (e.g., cat + tail)
+- **Interactive web interface** - Real-time Gradio UI with debug visualization
+
+---
+
+## How It Works
+
+The pipeline has three stages:
+
+1. **Segmentation (SAM2)** - Generates candidate object masks across the image
+2. **Matching (CLIP)** - Scores each segment against your text query
+3. **Inpainting (SDXL)** - Fills the masked region with contextually appropriate content
+
+---
+
+## Installation
+
+### Requirements
+
+- Python 3.8+
+- CUDA GPU with 12GB+ VRAM (recommended)
+- ~10GB disk space for models
+
+### Setup
+
+```bash
+# Clone repository
+git clone https://github.com/lxzcpro/TextEraser.git
+cd TextEraser
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the app
+python app.py
+```
+
+On first run, models will auto-download from HuggingFace (~10GB total).
+
+---
+
+## Usage
+
+### Web Interface
+
+1. Launch the app: `python app.py`
+2. Upload an image
+3. Enter what to remove (e.g., "bottle", "car", "person")
+4. Optionally specify background fill (default: "background")
+5. Click "Run Pipeline"
+6. Check the debug tab to see what was detected
+
+### Python API
+
+```python
+from src.pipeline import ObjectRemovalPipeline
+from PIL import Image
+import numpy as np
+
+# Initialize pipeline
+pipeline = ObjectRemovalPipeline()
+
+# Load and process image
+image = np.array(Image.open("photo.jpg"))
+result, mask, message = pipeline.process(
+    image=image,
+    text_query="bottle",
+    inpaint_prompt="table surface"
+)
+
+# Save result
+Image.fromarray(result).save("result.jpg")
+```