rf-detr 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.
rf-detr
Real-time transformer models for object detection, segmentation and keypoints
yolov3
PyTorch implementation of YOLOv3, YOLOv3-SPP and YOLOv3-tiny object detectors
Verdict
rf-detr ranks higher, 81 to 64. Of the 4 score parts we measure for both, rf-detr leads on 2, yolov3 on 1 and 1 is even.
- Adoptionyolov3 leads by 3 points, 57 to 54: close to even.
- 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 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 | rf-detr | yolov3 |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 54, popular | 57, popular |
| Freshness | 100, active | 70, recent |
| Maintenance | 94, healthy | 63, fair |
| Agent-ready | 70, partly | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 10.1k | 10.6k |
| License | Apache-2.0 (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2021 |
| 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
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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