# amdegroot/ssd.pytorch

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5,224 stars · 1,724 forks · Python · MIT

## Links

- GitHub: https://github.com/amdegroot/ssd.pytorch
- awesome-repositories: https://awesome-repositories.com/repository/amdegroot-ssd-pytorch.md

## Topics

`computer-vision` `deep-learning` `image-recognition` `machine-learning` `object-detection` `pytorch` `ssd` `webcam`

## Description

This is a PyTorch object detection framework that implements the Single Shot MultiBox Detector for identifying and localizing multiple objects within images and video. The project provides a neural network architecture designed for single-shot object detection, which predicts bounding boxes and class labels in one pass.

The implementation includes a real-time object detector capable of processing live video streams to track and label objects across sequential frames. It also features a complete computer vision training pipeline for preparing image datasets and training model weights.

The framework covers the end-to-end workflow for vision tasks, including dataset loading, deep learning model training with hardware acceleration, and quantitative performance evaluation using dedicated metrics and scripts.

## Tags

### Part of an Awesome List

- [Single Shot Detectors](https://awesome-repositories.com/f/awesome-lists/ai/single-shot-detectors.md) — Implements a single-shot detector architecture that predicts bounding boxes and class labels in a single pass.
- [Object Detection Frameworks](https://awesome-repositories.com/f/awesome-lists/ai/object-detection-frameworks.md) — Serves as a comprehensive framework for implementing the Single Shot MultiBox Detector for object identification in images and video.
- [Computer Vision](https://awesome-repositories.com/f/awesome-lists/ai/computer-vision.md) — Single Shot MultiBox Detector implementation.
- [Computer Vision Models](https://awesome-repositories.com/f/awesome-lists/ai/computer-vision-models.md) — Implementation of the Single Shot MultiBox Detector.
- [Model Implementations](https://awesome-repositories.com/f/awesome-lists/ai/model-implementations.md) — Single shot multibox detector implementation.
- [Object Detection](https://awesome-repositories.com/f/awesome-lists/more/object-detection.md) — Listed in the “Object Detection” section of the The Incredible Pytorch awesome list.

### Artificial Intelligence & ML

- [Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/object-detection.md) — Identifies and localizes multiple objects within images by generating bounding boxes and class labels. ([source](https://github.com/amdegroot/ssd.pytorch#readme))
- [Real-Time Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/real-time-object-detection.md) — Processes live video streams in real time to identify and track objects across sequential frames. ([source](https://github.com/amdegroot/ssd.pytorch#readme))
- [Detection Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/detection-model-training.md) — Optimizes model weights for object detection using standard datasets, configurable learning rates, and GPU acceleration. ([source](https://github.com/amdegroot/ssd.pytorch/blob/master/README.md))
- [Object Detection Training](https://awesome-repositories.com/f/artificial-intelligence-ml/deep-learning-training-toolsets/object-detection-training.md) — Provides a training pipeline to teach the model to recognize object classes using labeled datasets and hardware acceleration.
- [PyTorch Tensor Operations](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-optimization-and-inference/hardware-and-acceleration/tensor-computing-libraries/pytorch-tensor-operations.md) — Utilizes PyTorch tensor operations and dynamic computational graphs for efficient gradient descent and hardware acceleration.
- [SSD Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/neural-network-implementations/ssd-implementations.md) — Implements the neural network architecture based on the Single Shot MultiBox Detector protocol for real-time localization.
- [PyTorch Computer Vision Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-computer-vision-pipelines.md) — Offers a complete PyTorch-based workflow for image dataset preparation, model training, and detection accuracy evaluation.
- [Anchor Box Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/anchor-box-systems.md) — Implements a system for defining reference bounding boxes of various scales and aspect ratios as object detection priors.
- [Multi-Scale Feature Pyramids](https://awesome-repositories.com/f/artificial-intelligence-ml/inference-scaling/resolution-scaling/hierarchical-feature-pyramids/multi-scale-feature-pyramids.md) — Extracts detections from multiple convolutional layers to capture objects across different spatial scales.
- [Smooth L1 Loss Calculators](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/architectures/neural-network-components/loss-functions/perceptual-loss/content-loss-calculators/focal-loss-calculators/detection-loss-calculators/smooth-l1-loss-calculators.md) — Uses a Smooth-L1 loss function to robustly regress bounding box coordinates while minimizing outlier influence.
- [Model Evaluation Metrics](https://awesome-repositories.com/f/artificial-intelligence-ml/machine-learning/infrastructure/model-evaluation-and-validation/model-evaluation-metrics.md) — Provides tools and metrics to quantitatively measure the performance and quality of trained detection models. ([source](https://github.com/amdegroot/ssd.pytorch#readme))
- [Object Detection Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/model-prediction-evaluation/object-detection-evaluators.md) — Includes evaluation scripts to measure the accuracy of predicted bounding boxes against ground truth.
- [Vision Dataset Preparation](https://awesome-repositories.com/f/artificial-intelligence-ml/training-dataset-preparation/vision-dataset-preparation.md) — Provides utilities for downloading, preparing, and loading image collections into the training pipeline. ([source](https://github.com/amdegroot/ssd.pytorch/blob/master/README.md))