rf-detr vs yolov5
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.
rf-detr
Real-time transformer models for object detection, segmentation and keypoints
yolov5
PyTorch YOLOv5 object detection, segmentation and classification models with training code
Verdict
rf-detr ranks higher, 79 to 76. Of the 4 score parts we measure for both, rf-detr leads on 2, yolov5 on 1 and 1 is even.
- Adoptionyolov5 leads by 50 points, 96 to 46: a wide gap.
- Freshnessrf-detr leads by 30 points, 100 to 70: a wide gap.
- Maintenancerf-detr leads by 31 points, 94 to 63: a wide gap.
- Agent-readyEven at 70.
Pick rf-detr if you are
Computer vision engineers needing real-time detection or segmentation models
- Detection, segmentation and keypoints share one Python API across Nano to 2XLarge sizes
- Apache 2.0 for the package and N/S/M/L detection and all segmentation models
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
| What we compare | rf-detr | yolov5 |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 46, known | 96, widely used |
| Momentum | Not scored for this kind | Not scored for this kind |
| Freshness | 100, active | 70, recent |
| Maintenance | 94, healthy | 63, fair |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 10k | 58.3k |
| License | Apache-2.0 (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2022 |
| Language | Python | Python |
| Docker | No | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Not stated | Not stated |
rf-detr
RF-DETR is a Python package (`pip install rfdetr`, Python 3.10+) from Roboflow that provides pretrained DINOv2-based transformer models for object detection, instance segmentation and keypoint detection (preview). Models come in sizes from Nano to 2XLarge, run through one API via classes like `RFDETRMedium`, and can be fine-tuned on custom datasets. Some sizes (Atto, Femto, Pico, XL, 2XL) are under the PML 1.0 license rather than Apache 2.0.
Who it is for: Computer vision engineers needing real-time detection or segmentation models
Strengths
- Detection, segmentation and keypoints share one Python API across Nano to 2XLarge sizes
- Apache 2.0 for the package and N/S/M/L detection and all segmentation models
- README benchmarks on COCO and RF100-VL, with T4 TensorRT FP16 latency per size
- Fine-tuning supported, with Colab notebook and Hugging Face Space provided
Weaknesses
- Atto, Femto, Pico, XL and 2XL detection models use PML 1.0, not Apache 2.0
- Keypoint detection is labeled preview
- Latency figures are T4 TensorRT only; CPU and edge numbers not given in the visible README
- NAS for your own dataset requires the Roboflow platform
- GPU optional
- Needs PyTorch, supervision
- Models: RF-DETR detection (Atto to 2XL), RF-DETR instance segmentation (Seg-N to Seg-2XL), RF-DETR keypoint (preview), DINOv2 backbone, COCO and custom datasets
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
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