PyTorchObject DetectionAGPL-3.0
yolov8n
by ultralytics
State-of-the-art real-time object detection — YOLO v8 Nano
3.2Mparams
Identifiers
Model ID
ultralytics/yolov8nFeature URI
mixpeek://image_extractor@v1/ultralytics_yolov8n_v1Overview
YOLOv8n is the nano variant of Ultralytics' YOLOv8 family, optimized for real-time inference while maintaining strong detection accuracy. It supports detection, segmentation, classification, and pose estimation tasks.
On Mixpeek, YOLOv8n is the fastest object detection option, ideal for processing large video libraries where throughput matters more than detecting every small object.
Architecture
CSPDarknet53 backbone with C2f modules, FPN+PAN neck, and decoupled head. Anchor-free detection with distribution focal loss. Nano variant uses depth=0.33, width=0.25.
Mixpeek SDK Integration
import { Mixpeek } from "mixpeek";
const mx = new Mixpeek({ apiKey: "API_KEY" });
await mx.collections.ingest({
collection_id: "my-collection",
source: { url: "https://example.com/video.mp4" },
feature_extractors: [{
name: "object_detection",
version: "v1",
params: {
model_id: "ultralytics/yolov8n"
}
}]
});Capabilities
- Real-time inference at 640px resolution
- 80 COCO object categories
- Multi-task: detection, segmentation, classification, pose
- Optimized for edge and mobile deployment
Use Cases on Mixpeek
Live video stream monitoring with sub-10ms latency
Sports analytics — track players, balls, equipment in real-time
Manufacturing QA — detect defects on production lines
Specification
FrameworkPyTorch
Organizationultralytics
FeatureObject Detection
Outputbbox + label
Modalitiesvideo, image
RetrieverObject Filter
Parameters3.2M
LicenseAGPL-3.0
Downloads/moN/A
Build a pipeline with yolov8n
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
Open Pipeline Builder