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Ge"}],published:"2024-01-30",conference:"CVPR 2024",citation_count:890,tasks:[{name:"Open-Vocabulary Object Detection",slug:"open-vocabulary-object-detection"},G],methods:[{name:"Contrastive Learning",slug:"contrastive-learning"}],repositories:[{url:"https://github.com/AILab-CVC/YOLO-World",stars:16800}]})},158342:{route_id:"158342",title:"Ultralytics YOLO: real-time detection framework and model zoo",content:"Official Ultralytics framework release covering YOLO11 and YOLOv12 deployment: pretrained COCO checkpoints from nano to x-large, TensorRT/ONNX/CoreML export paths, and reproducible latency benchmarks on T4, Jetson Orin, and CPU targets. Widely used as the practical baseline for real-time detection deployments.",content_truncated:!1,metadata:V({source:"external",source_url:"https://docs.ultralytics.com/models/",authors:[{name:"Ultralytics"}],published:"2025-01-02",tasks:[G,J],repositories:[{url:"https://github.com/ultralytics/ultralytics",stars:46e3}],project_pages:[{url:"https://docs.ultralytics.com"}]})}},Ze=l=>l.map(d=>Nt[d]),We=Ze(["2502.12524","2407.17140","2410.13842","2406.03459","2304.08069","2405.14458","2401.17270","158342"]),Ve=Ze(["2410.13842","2502.12524","2407.17140","2405.14458","2304.08069"]),Ge="The user wants the current state-of-the-art for real-time object detection. That's a moving target split across two lineages — the YOLO family (fast CNN one-stage detectors) and the DETR family (transformer, end-to-end, NMS-free). I should ground the answer in recent papers benchmarked on COCO with reported latency or FPS, not just accuracy. Let me start with a broad hybrid search, see which papers surface, then run a second, narrower query to pin down the head-to-head COCO mAP vs latency trade-offs before I write anything.",Ke="Good — both lineages are well represented. YOLOv12, YOLOv10 and RT-DETRv2 / D-FINE keep coming up. To actually rank them I need the accuracy-vs-latency numbers side by side on the same COCO val2017 protocol, so let me narrow the query to the benchmark comparison specifically.",$e=`There isn't a single winner — real-time object detection in 2026 is a two-horse race between the [YOLO family](paper:2502.12524) (fast one-stage CNNs) and the end-to-end [DETR family](paper:2304.08069) (NMS-free transformers). Which one is “SOTA” depends entirely on where you sit on the accuracy-vs-latency curve.

**At the fast / edge end**, [YOLOv12](paper:2502.12524) is the current leader. Its area-attention design pushes attention into the YOLO backbone without the usual latency hit — YOLOv12-N reaches 40.6 mAP at ~1.6 ms on a T4, roughly +1.5 mAP over [YOLOv10](paper:2405.14458) at matched speed. [YOLOv10](paper:2405.14458) is still very relevant because it made the YOLO head NMS-free, cutting real end-to-end latency.

**At the high-accuracy end**, the transformer detectors win. [D-FINE](paper:2410.13842) currently holds the accuracy-latency Pareto front, reporting 55.8 mAP by reformulating box regression as fine-grained distribution refinement with self-distillation. It builds on [RT-DETRv2](paper:2407.17140), which hardened the [original RT-DETR](paper:2304.08069) recipe (the paper that first showed DETRs beating YOLOs in real time) with training-only “bag-of-freebies” that add no inference cost.

**My recommendation:** for edge or high-FPS deployment, start with [YOLOv12](paper:2502.12524) — the [Ultralytics framework](paper:158342) has ready-made checkpoints and export paths. If you have GPU headroom and want maximum accuracy while staying real-time, use [D-FINE](paper:2410.13842) or [RT-DETRv2](paper:2407.17140). And if you need open-vocabulary detection rather than fixed COCO classes, [YOLO-World](paper:2401.17270) is the one to look at.`,At=["How do these compare on edge devices like Jetson?","Show me open-vocabulary detectors","What about small-object detection?"];async function*Dt(l,d){const h=()=>(d==null?void 0:d.aborted)===!0,M=l.trim().slice(0,90)||"state-of-the-art real-time object detection";if(yield{type:"stream_start",request_id:"req_mock",session_id:null},await P(350,d),!h()){yield{type:"reasoning_start"};for(let f=0;f<Ge.length;f+=6){if(await P(14,d),h())return;yield{type:"reasoning_delta",text:Ge.slice(f,f+6)}}if(await P(400,d),!h()&&(yield{type:"reasoning_end"},await P(300,d),!h()&&(yield{type:"tool_call",id:"call_1",name:"search_papers",args:{query:M,top_k:8}},await P(1100,d),!h()&&(yield{type:"tool_result",id:"call_1",hits:We.length,took_ms:214,results:We},await P(350,d),!h())))){yield{type:"reasoning_start"};for(let f=0;f<Ke.length;f+=6){if(await P(14,d),h())return;yield{type:"reasoning_delta",text:Ke.slice(f,f+6)}}if(await P(350,d),!h()&&(yield{type:"reasoning_end"},await P(300,d),!h()&&(yield{type:"tool_call",id:"call_2",name:"search_papers",args:{query:"RT-DETR vs YOLOv12 COCO mAP latency FPS T4 TensorRT comparison",top_k:5}},await P(900,d),!h()&&(yield{type:"tool_result",id:"call_2",hits:Ve.length,took_ms:176,results:Ve},await P(400,d),!h())))){for(let f=0;f<$e.length;f+=4){if(await P(11,d),h())return;yield{type:"answer_delta",text:$e.slice(f,f+4)}}yield{type:"done",followups:At,finish_reason:"success"}}}}}const me=()=>{const d=String("0").trim().toLowerCase();return d==="1"||d==="true"||d==="yes"};class et extends Error{constructor(d,h){super(d),this.code=h,this.name="ChatStreamError"}}function Pt(l,d){if(me())try{fetch(ke(`/api/v1/chat/latency/${encodeURIComponent(l)}`),{method:"POST",headers:{"Content-Type":"application/json"},credentials:"include",keepalive:!0,body:JSON.stringify(d)}).catch(()=>{})}catch{}}function Mt(l,d={}){return me()?Yt(l,d.signal):Dt(l.message,d.signal)}async function*Yt(l,d){const h=await fetch(ke("/api/v1/chat/stream"),{method:"POST",headers:{"Content-Type":"application/json",Accept:"text/event-stream"},credentials:"include",body:JSON.stringify(l),signal:d});if(!h.ok||!h.body){const C=await h.json().catch(()=>null),x=C==null?void 0:C.detail,_=h.status===403&&!x;throw new et(_?"Chat on staging is limited to approved Hugging Face users. Sign in with an approved account and try again.":(x==null?void 0:x.message)||x||`Chat stream failed (${h.status})`,(x==null?void 0:x.code)||(_?"staging_access_required":void 0))}const M=h.body.getReader(),f=new TextDecoder;let L="";try{for(;;){const{done:C,value:x}=await M.read();if(C)break;L+=f.decode(x,{stream:!0});let _;for(;(_=L.indexOf(`

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