#55th of 52 in Open models
yolov5
PyTorch YOLOv5 object detection, segmentation and classification models with training code
- Stars
- 58.2k
- License
- AGPL-3.0
- Last commit
- Nov 2026
- Last release
- Nov 2022
- Language
- Python
56,557 on 4 Apr 202658,248 on 31 Oct 2026
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Ultralytics is hiring: 15 open roles
Overview
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
What it needs
- GPU optional
- Needs Python>=3.8, PyTorch>=1.8
- Models: PyTorch .pt, ONNX, TensorRT, CoreML, TFLite
Quick answers
Is yolov5 open source?
Yes. Its code is on GitHub under the AGPL-3.0 license.
Does yolov5 need a GPU?
No. It runs on a CPU and can use a GPU when you have one.
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