yolo26n
by ultralytics
Edge-optimized NMS-free object detector with 43% faster CPU inference
ultralytics/yolo26nmixpeek://image_extractor@v1/ultralytics_yolo26n_v1Deploy yolo26n
Single-tenantMixpeek has no managed extractor for this model. On a single-tenant deployment you upload the weights and a custom plugin serves them next to the rest of your pipeline.
Overview
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.
How it runs
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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