# eriklindernoren/pytorch-yolov3

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7,439 stars · 2,594 forks · Python · GPL-3.0

## Links

- GitHub: https://github.com/eriklindernoren/PyTorch-YOLOv3
- awesome-repositories: https://awesome-repositories.com/repository/eriklindernoren-pytorch-yolov3.md

## Description

This project is a PyTorch implementation of the YOLOv3 object detection architecture. It functions as a real-time object detector and computer vision framework designed to identify and locate multiple objects within images using bounding boxes and class labels.

The system allows for both the use of pretrained weights for immediate image analysis and the training of custom models using datasets with bounding box annotations. It provides a programmatic interface to integrate detection capabilities directly into other software applications.

The framework includes tools for model evaluation to measure accuracy through mean Average Precision and supports multi-scale feature prediction to identify targets of varying sizes.

## Tags

### Artificial Intelligence & ML

- [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) — Identifies and locates multiple objects within images in real-time using pretrained weights. ([source](https://github.com/eriklindernoren/pytorch-yolov3#readme))
- [Object Detection](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-systems/computer-vision/object-detection-tracking/object-detection.md) — Provides a programmatic interface to identify and locate objects within images using bounding boxes. ([source](https://github.com/eriklindernoren/pytorch-yolov3#readme))
- [Detection Model Training](https://awesome-repositories.com/f/artificial-intelligence-ml/custom-model-training/detection-model-training.md) — Allows training of object detection models on custom image datasets using YOLO format annotations. ([source](https://github.com/eriklindernoren/pytorch-yolov3#readme))
- [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) — Leverages PyTorch tensor operations for automatic differentiation and hardware-accelerated model computation.
- [Darknet-53 Backbones](https://awesome-repositories.com/f/artificial-intelligence-ml/modular-backbone-architectures/darknet-53-backbones.md) — Uses the Darknet-53 convolutional network as the primary backbone for hierarchical feature extraction.
- [Object Detection Models](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-computer-vision-pipelines/object-detection-models.md) — Provides a neural network model built with PyTorch for visual recognition tasks.
- [PyTorch Implementations](https://awesome-repositories.com/f/artificial-intelligence-ml/pytorch-implementations.md) — Implements the YOLOv3 architecture using the PyTorch framework for research and development.
- [Anchor Box Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/anchor-box-systems.md) — Implements a system for defining reference bounding boxes to improve object localization accuracy.
- [Average Precision Calculators](https://awesome-repositories.com/f/artificial-intelligence-ml/average-precision-calculators.md) — Provides tools to calculate mean Average Precision by integrating precision-recall curves for model evaluation.
- [Computer Vision Evaluation Tools](https://awesome-repositories.com/f/artificial-intelligence-ml/computer-vision-evaluation-tools.md) — Provides a framework for measuring the accuracy of object detection models via standard performance metrics.
- [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 object detections at three different resolutions to identify targets of varying sizes.
- [Detection Accuracy Evaluators](https://awesome-repositories.com/f/artificial-intelligence-ml/recognition-accuracy-evaluation/rating-accuracy-evaluators/detection-accuracy-evaluators.md) — Includes tools to measure model performance on test datasets using mean Average Precision. ([source](https://github.com/eriklindernoren/pytorch-yolov3#readme))
- [Weight Serialization](https://awesome-repositories.com/f/artificial-intelligence-ml/weight-reconstruction/weight-serialization.md) — Implements binary serialization to load pretrained model weights for immediate deployment.

### Part of an Awesome List

- [Object Detection](https://awesome-repositories.com/f/awesome-lists/ai/object-detection.md) — Provides a comprehensive system for identifying and locating objects using bounding boxes and class labels.
- [YOLOv3 Implementations](https://awesome-repositories.com/f/awesome-lists/ai/object-detection-frameworks/yolov3-implementations.md) — Implements the YOLOv3 object detection architecture for real-time image analysis.
- [Computer Vision Libraries](https://awesome-repositories.com/f/awesome-lists/ai/computer-vision-libraries.md) — Minimal PyTorch implementation of YOLOv3.