yolov5 vs insightface
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
insightface
Face detection, recognition and alignment toolbox with ArcFace and RetinaFace models
Verdict
yolov5 ranks higher, 76 to 64. Of the 4 score parts we measure for both, yolov5 leads on 3, insightface on 1.
- Adoptionyolov5 leads by 8 points, 96 to 88.
- Freshnessinsightface leads by 30 points, 100 to 70: a wide gap.
- Maintenanceyolov5 leads by 4 points, 63 to 59: close to even.
- Agent-readyyolov5 leads by 70 points, 70 to 0: a wide gap.
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 insightface if you are
Engineers building face recognition, detection or video privacy pipelines
- Covers recognition, detection and alignment with training code, datasets and IJB/Megaface evaluation scripts
- Python package runs inference with automatic CoreML, CUDA or CPU provider selection
| What we compare | yolov5 | insightface |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 96, widely used | 88, widely used |
| Freshness | 70, recent | 100, active |
| Maintenance | 63, fair | 59, fair |
| Agent-ready | 70, partly | 0, none |
| Facts from GitHub and the README | ||
| Stars | 58.3k | 30.2k |
| License | AGPL-3.0 (copyleft) | no license (read the license) |
| Last commit | Nov 2026 | Oct 2026 |
| Last release | Nov 2022 | Sep 2026 |
| 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
insightface
InsightFace is a 2D and 3D face analysis toolbox built on PyTorch and MXNet. It includes training code, datasets and pretrained models for recognition (ArcFace, PartialFC), detection (RetinaFace, SCRFD) and alignment, plus a Python package, a desktop GUI and a self-hosted server with REST API. Code is MIT, but training data and the models trained on it are non-commercial research only.
Who it is for: Engineers building face recognition, detection or video privacy pipelines
Strengths
- Covers recognition, detection and alignment with training code, datasets and IJB/Megaface evaluation scripts
- Python package runs inference with automatic CoreML, CUDA or CPU provider selection
- InsightFace Server offers a Web UI, REST API and SQLite storage in one Linux x86_64 container
- PrivateFrame blurs or mosaics faces in local video and can redact selectively from reference photos
Weaknesses
- Models and training data are non-commercial research only; commercial use needs separate licensing
- Inswapper and buffalo_l models require contacting the maintainers for a license
- Repository mixes many research subprojects on PyTorch and MXNet, so setup differs per module
- Server runs on Linux x86_64 only and needs operator-supplied, manifest-verified models
- GPU optional
- Needs PyTorch, MXNet, ONNX Runtime, SQLite
- Models: ONNX, PyTorch, MXNet
More in Open models
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.