laya 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.
laya
Single-pass multilingual classifier for typed choice, score and yes/no decisions
yolov5
PyTorch YOLOv5 object detection, segmentation and classification models with training code
Verdict
laya ranks higher, 88 to 76. Of the 4 score parts we measure for both, laya leads on 2, yolov5 on 2.
- Adoptionyolov5 leads by 7 points, 96 to 89.
- Freshnesslaya leads by 30 points, 100 to 70: a wide gap.
- Maintenancelaya leads by 29 points, 92 to 63: a wide gap.
- Agent-readyyolov5 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 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 | laya | yolov5 |
|---|---|---|
| Score parts, out of 100 | ||
| Adoption | 89, widely used | 96, widely used |
| Momentum | Not scored for this kind | Not scored for this kind |
| Freshness | 100, active | 70, recent |
| Maintenance | 92, healthy | 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 | Nov 2026 | Nov 2026 |
| Last release | Nov 2026 | Nov 2022 |
| 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
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
More in Open models
Building with these?
We help teams pick an open-source AI stack and run it in production. Client work never changes a rank.