#2222nd of 52 in Open models
yolov3
PyTorch implementation of YOLOv3, YOLOv3-SPP and YOLOv3-tiny object detectors
- Stars
- 10.6k
- License
- AGPL-3.0
- Last commit
- Nov 2026
- Last release
- Nov 2021
- Language
- Python
10,492 on 4 Apr 202610,631 on 31 Oct 2026
From GitHub's daily star counts.
Checked on GitHub.
Ultralytics is hiring: 15 open roles
Overview
PyTorch code for the three classic YOLOv3 object detection models, with scripts for training, validation, inference and export. Pretrained COCO checkpoints (80 classes) download automatically, and models can be exported to TorchScript, ONNX, OpenVINO, TensorRT, CoreML and PaddlePaddle. It reuses shared utilities from the ultralytics package.
Who it is for: Engineers needing a YOLOv3 detection baseline to train, run or export
Strengths
- Three variants (YOLOv3, SPP, tiny) with COCO-pretrained weights that download automatically
- Export to ONNX, TensorRT, OpenVINO, CoreML, TorchScript and PaddlePaddle via export.py
- detect.py accepts webcam, video, screenshot, glob and RTSP/RTMP/HTTP stream sources
- Models defined in YAML files, so architectures can be edited without Python changes
Weaknesses
- AGPL-3.0; commercial use without AGPL obligations needs a paid Enterprise License
- Last release was 2021-11-14, though commits continue
- Covers only the 2018-era YOLOv3 architecture; newer Ultralytics models live in a separate repo
- README gives no VRAM or RAM figures and no benchmark numbers
What it needs
- GPU optional
- Needs Python>=3.8, PyTorch>=1.8
- Models: YOLOv3, YOLOv3-SPP, YOLOv3-tiny, COCO, ONNX
Quick answers
Is yolov3 open source?
Yes. Its code is on GitHub under the AGPL-3.0 license.
Does yolov3 need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
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