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+# 3D Object Dimension & Volume Estimator
+
+This project is a Streamlit web application that estimates the 3D dimensions (Length, Width, Height) and Volume of objects from user-uploaded images. It leverages Detectron2 for object detection and instance segmentation, and a custom Convolutional Neural Network (CNN) trained on the Pix3D dataset for dimension regression.
+
+## Features
+
+* Upload single or multiple images of an object (different views).
+* Detects objects using a pre-trained Detectron2 (Mask R-CNN) model.
+* Displays segmentation masks and 2D bounding boxes for detected objects.
+* For the largest detected object in each view:
+    * Crops the object using its segmentation mask.
+    * Feeds the cropped patch to a custom CNN to predict dimensions (L, W, H, V).
+* Displays individual dimension predictions for each view.
+* Calculates and displays aggregated (averaged) dimensions if multiple views are provided.
+* User-friendly web interface built with Streamlit.
+
+## Models Used
+
+1.  **Object Detection & Segmentation:**
+    * **Detectron2 (Mask R-CNN R50-FPN 3x):** Pre-trained on the COCO dataset. Used to identify objects and generate pixel-wise segmentation masks.
+2.  **Dimension Estimation:**
+    * **Custom CNN (ResNet50 backbone):** Trained on image patches derived from the Pix3D dataset. The model takes a cropped image patch of an object as input and outputs its estimated Length, Width, Height (in meters), and Volume (in meters³).
+
+## Setup and Installation
+
+Follow these steps to set up and run the application locally:
+
+**1. Clone this GitHub Repository:**
+
+```bash
+git clone [https://github.com/YourUsername/YourGitHubRepoName.git](https://github.com/YourUsername/YourGitHubRepoName.git)
+cd YourGitHubRepoName