# ssd

**Repository Path**: snowjake/ssd

## Basic Information

- **Project Name**: ssd
- **Description**: ssd
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2021-10-09
- **Last Updated**: 2026-03-26

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# TensorFlow YOLOv3 Object Detection Project

## Project Overview

This project implements the YOLOv3 object detection algorithm based on the TensorFlow framework. YOLOv3 (You Only Look Once v3) is an efficient single-stage object detection algorithm that achieves real-time detection while maintaining high accuracy.

## Features

- **Complete YOLOv3 Architecture**: Includes the Darknet53 backbone network and three scale detection layers
- **Multi-Dataset Support**: Supports datasets such as VOC and COCO
- **Model Conversion Tool**: Supports conversion from Darknet weights to TensorFlow checkpoints
- **Training and Evaluation**: Provides a complete training pipeline and mAP evaluation tools
- **Image/Video Inference**: Supports object detection inference on images and videos

## System Requirements

- Python 3.6+
- TensorFlow 1.x / 2.x
- OpenCV
- NumPy
- Pillow

Install dependencies:
```bash
pip install -r docs/requirements.txt
```

## Project Structure

```
tensorflow-yolov3/
├── core/                    # Core modules
│   ├── backbone.py          # Darknet53 backbone network
│   ├── yolov3.py            # YOLOv3 model definition
│   ├── dataset.py           # Dataset loading and preprocessing
│   ├── utils.py             # Utility functions
│   └── config.py            # Configuration file
├── data/                    # Data directory
│   ├── classes/             # Class definition files
│   ├── anchors/             # Anchor files
│   └── dataset/             # Dataset index files
├── scripts/                 # Auxiliary scripts
├── mAP/                     # Evaluation tools
├── train.py                 # Training script
├── evaluate.py              # Evaluation script
├── image_demo.py            # Image detection demo
└── video_demo.py            # Video detection demo
```

## Usage Instructions

### 1. Data Preparation

Organize your dataset in VOC format and create training and testing index files under the `data/dataset/` directory.

### 2. Model Training

```bash
python train.py
```

### 3. Model Inference

Image detection:
```bash
python image_demo.py --image_path your_image.jpg
```

Video detection:
```bash
python video_demo.py --video_path your_video.mp4
```

### 4. Model Evaluation

```bash
python evaluate.py
```

## Core Module Descriptions

### Model Architecture (core/yolov3.py)

- `YOLOV3` class: Full implementation of the YOLOv3 model
- Supports multi-scale feature fusion
- Implements GIoU loss and Focal Loss

### Data Processing (core/dataset.py)

- `Dataset` class: Data loading and augmentation
- Supports data augmentation methods including random horizontal flipping, random cropping, and random translation

### Utility Functions (core/utils.py)

- Image preprocessing
- Non-Maximum Suppression (NMS)
- Boundary box IOUs calculation
- Post-processing of model outputs

## Pretrained Models

The project supports conversion from Darknet pretrained weights:

```bash
python from_darknet_weights_to_ckpt.py
```

## License

This project is for learning and research purposes only.