Ssd: Train and evaluate SSD object detectors in PyTorch

Understand the project

Practical overview

Use it when you need a local PyTorch research codebase for SSD training, evaluation or image-folder inference, rather than a hosted model endpoint.

Computer-vision researchers studying or adapting SSD-based object detection.
Developers who want configurable PyTorch code for training, evaluating or running inference with SSD models.

Train an SSD model using Pascal VOC or COCO data.
Evaluate a configured model, including evaluation during training.
Run folder-based inference and save images annotated with predicted boxes, scores and labels.
Modify model components such as the backbone, detector, box head or predictor for experiments.

Before you choose

The repository is public and lists an MIT license.

The documented stack includes Python 3, PyTorch 1.0 or later, yacs, Vizer, GCC 4.9 or later and OpenCV; installation instructions also list torchvision and tqdm.
Training and evaluation examples use GPU workflows, while CPU support is documented for inference.
VOC or COCO training requires preparing the dataset directory structure described in the repository.
The repository describes checkpoint download and caching for inference and lists pretrained model links.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Generate object-detection annotations for images in a folder using the documented demo and checkpoint example.

Python 3 and the listed dependencies: PyTorch 1.0 or later, torchvision, yacs, tqdm, opencv-python and Vizer.
A cloned copy of the repository and images in a folder such as the documented demo directory.

Clone the repository and enter its directory.
Install dependencies with the command below.
Documented command
```
pip install -r requirements.txt
```
Run the documented demo.py example with its configuration file, image-folder argument and checkpoint URL.

The example downloads and caches the checkpoint and saves predicted images with boxes, scores and label names under `demo/result` by default.

The documented example uses a specific VGG SSD300 VOC checkpoint and configuration; it does not establish results for other checkpoints or datasets.
This describes the repository's documented workflow and is not a test of execution.

Evidence and transparency

Source record details

Source availability: Active
Sources checked: 16 October 2026
Original website title: lufficc/SSD · Official homepage title · retained in its source language