timesfm 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.
timesfm
Pretrained time-series foundation model for zero-shot forecasting from Google Research
yolov5
PyTorch YOLOv5 object detection, segmentation and classification models with training code
Verdict
timesfm ranks higher, 78 to 76. Of the 4 score parts we measure for both, timesfm leads on 1, yolov5 on 3.
- Adoptionyolov5 leads by 6 points, 96 to 90.
- Freshnesstimesfm leads by 30 points, 100 to 70: a wide gap.
- Maintenanceyolov5 leads by 3 points, 63 to 60: close to even.
- Agent-readyyolov5 leads by 40 points, 70 to 30: a wide gap.
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
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 | timesfm | yolov5 |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 90, widely used | 96, widely used |
| Momentum | Not scored for this kind | Not scored for this kind |
| Freshness | 100, active | 70, recent |
| Maintenance | 60, fair | 63, fair |
| Agent-ready | 30, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 35.4k | 58.3k |
| License | Apache-2.0 (permissive) | AGPL-3.0 (copyleft) |
| Last commit | Sep 2026 | Nov 2026 |
| Last release | Aug 2026 | Nov 2022 |
| Language | Python | Python |
| Docker | No | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
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)
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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