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