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Ssd.pytorch

Ssd.pytorch is a Python implementation of the Single Shot MultiBox Detector (SSD) in PyTorch, providing code to train, evaluate and demonstrate an image object-detection model.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Ssd.pytorch — OpenViST project profile in AI Models.
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Understand the project

Practical overview

When it fits

Use it when you want code for working with SSD object detection in a local Python and PyTorch project, including documented training, evaluation and demo workflows.

Who it is for

  • Computer-vision developers exploring or adapting an SSD object detector.
  • Python and PyTorch users who want to train or evaluate SSD with VOC or COCO data.
  • Developers building a notebook or webcam-based object-detection demonstration.

Documented uses

  • Train SSD using the documented VOC or COCO datasets.
  • Evaluate a trained network with the repository's evaluation script.
  • Try detection through the notebook or live webcam demo.
Before you choose

Requirements & limits

The repository is public and identifies its license as MIT.

  • The README specifies Python 3 or later and PyTorch.
  • Training documentation covers VOC and COCO; custom-dataset training is listed as future work.
  • Training uses fc-reduced VGG-16 base-network weights, and an NVIDIA GPU is strongly recommended for speed.
  • The live demo requires OpenCV Python bindings and an onboard webcam; the README describes CPU and NVIDIA GPU use.

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

Set up the webcam detection demo

Prepare the documented local requirements and launch the live webcam demo.

Before you start

  • Python 3 or later
  • PyTorch
  • A cloned ssd.pytorch repository
  • Python bindings for OpenCV 2 or later
  • An onboard webcam

Steps

  1. Install PyTorch for your environment and clone the repository.

  2. Install OpenCV with Python bindings.

  3. From the repository, run the command below.

    Documented command
    python -m demo.live

Expected outcome

The README says the demo opens the webcam and begins detecting; CPU and NVIDIA GPU use are described.

Guide limitations

  • The README notes that cv2.waitkey may need adjustment for optimal frame rate.
  • The default webcam can be changed in demo/live.py.
Documentation 1 ↗
Source-based practical guide

Prepare and start SSD training

Follow the documented setup and start the training script.

Before you start

  • Python 3 or later
  • PyTorch
  • A cloned ssd.pytorch repository
  • VOC or COCO training data
  • The fc-reduced VGG-16 PyTorch base-network weights

Steps

  1. Install PyTorch and clone the repository.

  2. Download and set up a supported VOC or COCO dataset using the repository's dataset instructions.

  3. Place vgg16_reducedfc.pth in the repository's weights directory.

  4. Run the command below, setting or editing the parameters described in train.py as needed.

    Documented command
    python train.py

Expected outcome

The training script starts with the configured dataset and base-network weights.

Guide limitations

  • An NVIDIA GPU is strongly recommended for training speed.
  • The README lists VOC and COCO as supported training datasets; custom-dataset training appears on its to-do list.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
16 October 2026
Original website title
amdegroot/ssd.pytorch · Official homepage title · retained in its source language
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