yolo26n
by ultralytics
Edge-optimized NMS-free object detector with 43% faster CPU inference
ultralytics/yolo26nmixpeek://image_extractor@v1/ultralytics_yolo26n_v1Overview
YOLO26 is Ultralytics' latest generation real-time object detector, engineered from the ground up for edge and low-power devices. It removes Decoupled Focal Loss (DFL) for simplified export, introduces end-to-end NMS-free inference for streamlined deployment, and uses ProgLoss + STAL for improved small-object accuracy. The MuSGD optimizer (SGD + Muon) delivers up to 43% faster CPU inference.
On Mixpeek, YOLO26 is the default object detection model for video analysis pipelines requiring real-time performance on edge hardware. Its NMS-free architecture eliminates a common deployment pain point, and the Nano variant runs on mobile and IoT devices while maintaining competitive detection accuracy.
Architecture
Attention-centric backbone with R-ELAN modules. NMS-free end-to-end inference via learned object queries. ProgLoss (progressive loss scaling) and STAL (Spatial-Temporal Attention Loss) for improved small-object detection. Available in Nano (N), Small (S), Medium (M), Large (L), and Extra Large (X) variants. Supports export to TensorRT, ONNX, CoreML, TFLite, and OpenVINO.
Mixpeek SDK Integration
// No extractor parameter takes a Hugging Face model id (checked against
// GET /v1/discovery/extractors, which returns 13), so yolo26n runs
// on your side and the output is upserted through POST
// /v1/namespaces/{namespace_id}/documents/upsert. On Enterprise the other
// path is to upload the weights instead: POST /v1/namespaces/{id}/models
// accepts the huggingface format and a custom plugin loads them.
const res = await fetch(
"https://api.mixpeek.com/v1/namespaces/ns_your_namespace/documents/upsert",
{
method: "POST",
headers: {
Authorization: "Bearer API_KEY",
"Content-Type": "application/json",
},
body: JSON.stringify({
collection_id: "col_your_collection",
documents: [
{
document_id: "asset-00412",
// Boxes, masks, depth maps and anomaly scores are structured
// results, not vectors. They go in payload and are reachable
// through pre_filters on a retriever, not through similarity.
payload: {
detections: modelOutput,
source_key: "archive/2026/asset-00412",
},
},
],
}),
},
);
// No managed alternative for an open label set. Two extractors do emit a
// bbox, for the one thing each detects: document_graph_extractor@v1 per
// layout block, face_identity_extractor@v1 per face. Nothing ships that
// returns masks, depth maps or anomaly scores.Capabilities
- End-to-end NMS-free inference for simplified deployment
- 43% faster CPU inference via MuSGD optimizer
- 5 model variants from Nano to Extra Large
- Object detection, instance segmentation, pose estimation, OBB, classification
- Export to TensorRT, ONNX, CoreML, TFLite, OpenVINO
Use Cases on Mixpeek
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| COCO val2017 | mAP@50 | ~52 (Nano) | Model card |
| COCO val2017 | mAP@50:95 | ~38 (Nano) | Model card |
Performance
Common Pipeline Companions
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Specification
Research Paper
Model paper or technical report
arxiv.orgBuild a pipeline with yolo26n
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
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