yolov5 vs yolov3
Two of the top projects in Open models, side by side: who leads on each score part and who each one suits, then setup, license, activity and what each review found.
yolov5
PyTorch YOLOv5 object detection, segmentation and classification models with training code
yolov3
PyTorch implementation of YOLOv3, YOLOv3-SPP and YOLOv3-tiny object detectors
Verdict
yolov5 ranks higher, 76 to 64. Of the 4 score parts we measure for both, yolov5 leads on 1, yolov3 on none and 3 are even.
- Adoptionyolov5 leads by 39 points, 96 to 57: a wide gap.
- FreshnessEven at 70.
- MaintenanceEven at 63.
- Agent-readyEven at 70.
Pick yolov5 if you are
Engineers training or deploying YOLO detection models on custom image data
- Five model sizes, from 1.9M to 86.7M params, with published CPU and V100 latency
- detect.py accepts webcam, video, screen, YouTube and RTSP/RTMP/HTTP sources
Pick yolov3 if you are
Engineers needing a YOLOv3 detection baseline to train, run or export
- Three variants (YOLOv3, SPP, tiny) with COCO-pretrained weights that download automatically
- Export to ONNX, TensorRT, OpenVINO, CoreML, TorchScript and PaddlePaddle via export.py
| What we compare | yolov5 | yolov3 |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 57, popular |
| Freshness | 70, recent | 70, recent |
| Maintenance | 63, fair | 63, fair |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 58.3k | 10.6k |
| License | AGPL-3.0 (copyleft) | AGPL-3.0 (copyleft) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2022 | Nov 2021 |
| Language | Python | Python |
| Docker | No | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
yolov5
YOLOv5 provides pretrained COCO checkpoints (n/s/m/l/x, plus 1280px P6 variants) and PyTorch scripts for detection, instance segmentation and classification. detect.py runs on webcams, files, folders, screen capture, YouTube and RTSP/RTMP/HTTP streams, train.py trains on custom data, and models can be exported to ONNX, TensorRT, CoreML and TFLite. The README points newer work to the separate ultralytics package.
Who it is for: Engineers training or deploying YOLO detection models on custom image data
Strengths
- Five model sizes, from 1.9M to 86.7M params, with published CPU and V100 latency
- detect.py accepts webcam, video, screen, YouTube and RTSP/RTMP/HTTP sources
- Export to ONNX, TensorRT, CoreML and TFLite, with a Jetson deployment guide
- Training extras: AutoBatch, multi-GPU, hyperparameter evolution, frozen layers, ClearML and Comet logging
Weaknesses
- Last tagged release is v7.0 from 2022-11-22
- AGPL-3.0 license; commercial use needs a paid Enterprise License
- No pose estimation or oriented boxes (OBB); README directs those to the ultralytics package
- COCO training takes about 1 to 8 days per model on one V100
- GPU optional
- Needs Python>=3.8, PyTorch>=1.8
- Models: PyTorch .pt, ONNX, TensorRT, CoreML, TFLite
yolov3
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
- GPU optional
- Needs Python>=3.8, PyTorch>=1.8
- Models: YOLOv3, YOLOv3-SPP, YOLOv3-tiny, COCO, ONNX
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