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YOLOV3

YOLOV3 is a Python/PyTorch implementation of YOLOv3, YOLOv3-SPP and YOLOv3-tiny, with tools for object-detection training, validation, inference and model export.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

YOLOV3 — OpenViST project profile in AI Models.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when you need a code-based workflow for YOLOv3-family object detection, from model training or validation through inference and export.

Who it is for

  • Developers building or adapting object detectors for images and video.
  • Computer-vision practitioners who need to train, validate or export YOLOv3-family models.

Documented uses

  • Train a custom image or video object detector.
  • Run detections on images, video, webcams, screenshots or streams.
  • Validate a trained model.
  • Export a model for a supported format such as ONNX, TensorRT or CoreML.
Before you choose

Requirements & limits

The public repository specifies an AGPL-3.0 license. Its README directs commercial users to request an Enterprise License.

  • Setup requires Python 3.8 or later, PyTorch 1.8 or later and the listed repository dependencies.
  • Training examples use COCO configuration files and note that batch size depends on available hardware.
  • The README lists TorchScript, ONNX, OpenVINO, TensorRT, CoreML and PaddlePaddle as export formats.

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 YOLOV3 repository

Clone the repository and install its Python dependencies.

Before you start

  • Python 3.8 or later
  • PyTorch 1.8 or later

Steps

  1. Clone https://github.com/ultralytics/yolov3.

  2. Change into the cloned yolov3 directory.

  3. Install dependencies with pip install -r requirements.txt.

Expected outcome

The repository dependencies are installed in the Python environment.

Guide limitations

  • This covers setup only; it does not run inference or training.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
YOLOV3Train a custom image or video object detector.Linux, macOS, WindowsThe public repository specifies an AGPL-3.0 license. Its README directs commercial users to request an Enterprise License.
Pulse Of MotionEstimate a video's physical frame rate from its visual dynamics.LinuxThe repository is public. Its README describes a released checkpoint that downloads from Hugging Face on first inference, with a manual download option. The strongest internal model is not fully released; the README directs interested users to contact the project about access. No license information is provided in the repository evidence.
Neural Image AssessmentTrain the model using AVA images and rating annotations.Cloud, Linux, macOS, Windows
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
13 October 2026
Original website title
ultralytics/yolov3 · Official homepage title · retained in its source language
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