Data checked 2026-08-13. Data may be stale - beyond the review cycle.
Review cycles are documented on the Methodology page. Methodology
ultralytics/yolov5
Software Tool Tier A perception_ai AGPL-3.0
Ultralytics YOLOv5 in PyTorch for object detection, instance segmentation, classification, training, and export.
Engineering Snapshot
Use cases & tasks 6
- Best suited for
-
- UAV perception pipelines
- Edge deployment on Jetson/embedded
- Rapid prototyping of detection models
- Primary tasks
-
- Object detection from aerial imagery
- Instance segmentation for obstacle avoidance
- Model export to ONNX/TensorRT/CoreML for inference
Stack & ecosystem
- Resource type
- Software Tool
- Ecosystem
- computer-vision · coreml · deep-learning · image-classification · inference · instance-segmentation
License & compliance
- License
-
AGPL-3.0(Inferred)
Lifecycle & freshness Show
- Maintenance
- Active
- Latest version
- Not recorded
- Last activity
- 2026-08-13
- Last checked
- 2026-08-13
- Verification
- Official confirmed
What It Solves
Real-time object detection, instance segmentation, and classification for UAV perception tasks.
Primary use cases
- Object detection from aerial imagery
- Instance segmentation for obstacle avoidance
- Model export to ONNX/TensorRT/CoreML for inference
Secondary use cases
- Training custom detectors on aerial datasets
- Integration with tracking (e.g., ByteTrack)
- Simulation-based synthetic data generation
When to Use
Consider when
- AGPL-3.0 license requires source disclosure for commercial use
- Ultralytics now maintains YOLOv8/YOLO11 as primary; YOLOv5 is mature but legacy
- Export to TensorRT requires manual optimization for specific hardware
Verify before adopting
- License compatibility with your project
- Inference latency on target hardware
- Dataset domain match (aerial vs COCO)
Where It Fits
Stack layer perception_localization
Start Here
Adoption Checklist
- Needs verification License compatibility with your project
- Needs verification Inference latency on target hardware
- Needs verification Dataset domain match (aerial vs COCO)
Each check stays "needs verification" until an official source confirms it; unconfirmed items are never marked verified.
Known Limitations & Unknowns
Known limitations
- AGPL-3.0 may restrict commercial deployment
- No built-in ROS/MAVLink integration
- Single-GPU training only (no native distributed)
- YOLOv5 architecture superseded by YOLOv8/YOLO11
Not publicly verified
- Long-term maintenance commitment for YOLOv5 vs newer Ultralytics versions
- Performance on thermal/infrared aerial datasets
Alternatives & Related Tools
Alternatives
- mmdetection — Alternative to
Related tools
- ultralytics — Owned by
- onnx-runtime — Integrates with
- openvino — Integrates with
- bytetrack — compatible with
- jetson-inference — compatible with
How is it used?
Start from the recorded entry points below, then validate against the technical checklist.
- Official site https://docs.ultralytics.com/yolov5/
- Repository https://github.com/ultralytics/yolov5
- Documentation https://docs.ultralytics.com/yolov5/
Technical checklist
- OK License identified Recorded: AGPL-3.0
- OK Maintenance signal Active
- OK Verification status Official confirmed
- OK Source evidence attached 1 source record(s)
- NEEDS REVIEW Latest version recorded Not recorded
Official Links
Metadata & Governance
| License | AGPL-3.0 — Inferred |
|---|---|
| Commercial model | Unknown |
| Maintenance status | Active |
| Verification status | Official confirmed — Confirmed via the official repository API responses in SourceRefs below. |
| Latest version | Not recorded |
| Latest release | Not recorded |
| Last activity | 2026-08-13 |
| Last checked | 2026-08-13 |
| First seen | Not recorded |
Dataset facts
Facts above come from the official dataset card only; unconfirmed fields stay unknown.
Related Resources & Dependencies
- ultralytics — owned by (confirmed)
- onnx-runtime — integrates with (verified)
- openvino — integrates with (verified)
- mmdetection — alternative to (verified)
- bytetrack — compatible with (verified)
- jetson-inference — compatible with (verified)
Recent Activity
New resource: ultralytics/yolov5
New repository resource added by the sprint promote pipeline.
Related Knowledge
Collections
- Aerial Perception Models and Datasets — Source-backed aerial dataset and model repositories for detection and tracking research and prototyping.
- Visual Perception Models and Data — Indexed model and dataset repositories for aerial visual perception research and prototyping.