laya vs rf-detr
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.
laya
Single-pass multilingual classifier for typed choice, score and yes/no decisions
rf-detr
Real-time transformer models for object detection, segmentation and keypoints
Verdict
laya ranks higher, 88 to 79. Of the 4 score parts we measure for both, laya leads on 1, rf-detr on 2 and 1 is even.
- Adoptionlaya leads by 43 points, 89 to 46: a wide gap.
- FreshnessEven at 100.
- Maintenancerf-detr leads by 2 points, 94 to 92: close to even.
- Agent-readyrf-detr leads by 40 points, 70 to 30: a wide gap.
Pick laya if you are
Engineers needing fast multilingual triage, routing and moderation classifiers
- One forward pass: 33 ms per question, 7.2 ms/question batched, measured on a T4
- No generation, so no output parsing; answers are typed choice, score or probability
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
| What we compare | laya | rf-detr |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 89, widely used | 46, known |
| Momentum | Not scored for this kind | Not scored for this kind |
| Freshness | 100, active | 100, active |
| Maintenance | 92, healthy | 94, healthy |
| Agent-ready | 30, minimal | 70, partly |
| Facts from GitHub and the README | ||
| Stars | 35.4k | 10k |
| License | Apache-2.0 (permissive) | Apache-2.0 (permissive) |
| Last commit | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2026 |
| Language | Python | Python |
| Docker | Yes | No |
| GPU | Optional | Optional |
| arm64 or Apple Silicon | Mentioned | Not stated |
laya
Laya answers typed questions (choice, score, noul) about a text, email, ticket or JSON state in one forward pass of an encoder model, with no text generation. A Router picks between three checkpoints (ModernBERT-large English, mmBERT-base multilingual for 100+ languages, and a typed-decisions fine-tune). It ships as a Python package with a CLI, optional HTTP and MCP servers, and LangChain, LlamaIndex and CrewAI integrations.
Who it is for: Engineers needing fast multilingual triage, routing and moderation classifiers
Strengths
- One forward pass: 33 ms per question, 7.2 ms/question batched, measured on a T4
- No generation, so no output parsing; answers are typed choice, score or probability
- Router selects English or multilingual checkpoint per request; 100+ languages covered
- Fine-tuning notebooks provided; typed-decisions checkpoint scores 0.766 vs 0.362 for base English
Weaknesses
- Zero-shot accuracy is low on domain tasks; fine-tuning is needed for good results
- Multilingual checkpoint ships with a 1,024-token limit; beyond about 4,000 tokens accuracy varies
- Only classifies fixed question types; cannot generate or explain text
- Checkpoints download from Hugging Face on first use, so offline setups need preparation
- GPU optional
- Docker + Compose
- Needs Hugging Face Hub (checkpoint download), PyTorch, transformers
- Models: ModernBERT-large (421M), mmBERT-base (322M), ONNX Runtime
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
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