| # 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://huggingface.co/suryaprakash01/dimension_Detect/edit/main | |
| cd YourGitHubRepoName |