# car-transformer

**Repository Path**: enterprise2021/car-transformer

## Basic Information

- **Project Name**: car-transformer
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-07-27
- **Last Updated**: 2025-03-03

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Multiview Keypoint Detection: A Deep Learning Approach

## Overview

Welcome to the **Multiview Keypoint Detection** project, a deep learning system designed to detect keypoints in images from multiple perspectives. This project leverages the power of convolutional neural networks (CNNs) and transformers to understand and process visual data across different spatial-temporal resolutions. The goal is to develop an algorithm capable of accurately identifying and localizing keypoints in paired images, which is crucial for applications in computer vision such as pose estimation, action recognition, and 3D reconstruction.

### Key Features

- **Multiview Learning:** The system processes two images simultaneously, capturing a comprehensive view of the scene.
- **ResNet Backbone:** Utilizes a pre-trained ResNet network for feature extraction.
- **Transformer Encoder:** Applies a transformer encoder to model interdependencies between features.
- **Fusion Module:** Integrates information from both images to form a consolidated feature representation.
- **Keypoint Head:** Predicts keypoint locations based on the fused features.
- **Custom Loss Function:** Implements a loss function that optimizes the network for keypoint detection tasks.

## License
This project is licensed under the MIT License - see the LICENSE file for details.