rf-detr vs timesfm
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
timesfm
Pretrained time-series foundation model for zero-shot forecasting from Google Research
Verdict
rf-detr ranks higher, 79 to 78. Of the 4 score parts we measure for both, rf-detr leads on 2, timesfm on 1 and 1 is even.
- Adoptiontimesfm leads by 44 points, 90 to 46: a wide gap.
- FreshnessEven at 100.
- Maintenancerf-detr leads by 34 points, 94 to 60: a wide gap.
- Agent-readyrf-detr leads by 40 points, 70 to 30: a wide gap.
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 timesfm if you are
Engineers and researchers forecasting time series without per-task training
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
| What we compare | rf-detr | timesfm |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 46, known | 90, widely used |
| Momentum | Not scored for this kind | Not scored for this kind |
| Freshness | 100, active | 100, active |
| Maintenance | 94, healthy | 60, fair |
| Agent-ready | 70, partly | 30, minimal |
| Facts from GitHub and the README | ||
| Stars | 10k | 35.4k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Sep 2026 |
| Last release | Nov 2026 | Aug 2026 |
| Language | Python | Python |
| Docker | No | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Not stated | Mentioned |
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
timesfm
TimesFM is a pretrained decoder-only model for time-series forecasting, installed as a Python package with PyTorch or MLX (Apple silicon) backends. Version 3.0 forecasts univariate and multivariate series with past-only and past-and-future covariates, returning point forecasts plus nine quantiles (0.1 to 0.9). The code is Apache-2.0, but 3.0 weights carry a non-commercial license.
Who it is for: Engineers and researchers forecasting time series without per-task training
Strengths
- Zero-shot forecasting of univariate and multivariate series with past-only and future covariates
- Returns point forecast plus 9 quantiles (0.1 to 0.9) per step
- MLX backend runs 3.0 on Apple silicon without PyTorch, numerically matched to PyTorch
- Includes LoRA fine-tuning example (HF Transformers + PEFT), unit tests and an agent SKILL.md
Weaknesses
- TimesFM 3.0 weights are non-commercial and non-production; commercial use only via Google Cloud services
- Not an officially supported Google product
- Forecasting only; contexts beyond 15,360 points are truncated to the most recent
- README code imports differ between 3.0 examples and the PyPI 2.0.2 note, which is unclear
- GPU optional
- Needs PyTorch or MLX, Hugging Face checkpoints
- Models: TimesFM 3.0 (PyTorch checkpoint), TimesFM 2.5 (200M params, 16k context), TimesFM 1.0 and 2.0 (v1 archive)
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