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This work introduces an attention-centric YOLO framework, namely YOLOv12, built around area attention and residual efficient layer aggregation networks (R-ELAN). YOLOv12-N reaches 40.6 mAP at 1.64 ms on a T4 GPU, closing the gap to attention-based detectors without the usual latency penalty and surpassing YOLOv10/YOLOv11 by +1.2–1.6 mAP at matched latency.",content_truncated:!1,metadata:ke({arxiv_id:"2502.12524",authors:[{name:"Y. Tian"},{name:"Q. Ye"},{name:"D. Doermann"}],published:"2025-02-18",citation_count:412,tasks:[Ie,Le],methods:[{name:"Area Attention",slug:"area-attention"}],repositories:[{url:"https://github.com/sunsmarterjie/yolov12",stars:18700}]})},"2407.17140":{route_id:"2407.17140",title:"RT-DETRv2: Improved Baseline with Bag-of-Freebies for Real-Time Detection",content:"RT-DETRv2 improves the real-time DETR baseline with selective multi-scale feature extraction, a discrete sampling operator to ease deployment, and training-only 'bag-of-freebies' that add no inference cost. RT-DETRv2-R18/R50/R101 reach 47.9 / 53.4 / 54.3 mAP on COCO at 217 / 108 / 74 FPS on T4 TensorRT FP16, remaining NMS-free end to end.",content_truncated:!1,metadata:ke({arxiv_id:"2407.17140",authors:[{name:"W. Lv"},{name:"Y. Zhao"},{name:"J. 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Zhang"}],published:"2024-06-05",citation_count:94,tasks:[Ie,Le],methods:[{name:"Vision Transformer",slug:"vision-transformer"}],repositories:[{url:"https://github.com/Atten4Vis/LW-DETR",stars:5200}]})},"2304.08069":{route_id:"2304.08069",title:"DETRs Beat YOLOs on Real-time Object Detection",content:"RT-DETR is the first real-time end-to-end object detector, built on an efficient hybrid encoder that decouples intra-scale interaction and cross-scale fusion, plus IoU-aware query selection. RT-DETR-R50 achieves 53.1 mAP at 108 FPS on COCO, beating comparably sized YOLO detectors in both speed and accuracy while eliminating NMS entirely; its Figure 1 accuracy–latency plot became the reference for later real-time detectors.",content_truncated:!1,metadata:ke({arxiv_id:"2304.08069",authors:[{name:"Y. Zhao"},{name:"W. Lv"},{name:"S. Xu"}],published:"2023-04-17",conference:"CVPR 2024",citation_count:2310,tasks:[Ie,Le],methods:[{name:"DETR",slug:"detr"}],repositories:[{url:"https://github.com/lyuwenyu/RT-DETR",stars:9400}]})},"2405.14458":{route_id:"2405.14458",title:"YOLOv10: Real-Time End-to-End Object Detection",content:"YOLOv10 removes NMS from the YOLO pipeline via consistent dual assignments during training and applies a holistic efficiency–accuracy driven design across the architecture. YOLOv10-S is 1.8x faster than RT-DETR-R18 at similar COCO accuracy; the family spans 46.3–54.4 mAP with substantially lower end-to-end latency than prior YOLOs because post-processing disappears.",content_truncated:!1,metadata:ke({arxiv_id:"2405.14458",authors:[{name:"A. Wang"},{name:"H. Chen"},{name:"L. Liu"}],published:"2024-05-23",conference:"NeurIPS 2024",citation_count:1650,tasks:[Ie,Le],methods:[{name:"NMS-free Training",slug:"nms-free-training"}],repositories:[{url:"https://github.com/THU-MIG/yolov10",stars:44200}]})},"2401.17270":{route_id:"2401.17270",title:"YOLO-World: Real-Time Open-Vocabulary Object Detection",content:"YOLO-World extends the YOLO paradigm to open-vocabulary detection through vision-language modeling with a re-parameterizable vision-language path aggregation network (RepVL-PAN) and region-text contrastive pretraining. It detects categories never seen at training time while running at 35+ FPS, and its prompt-then-detect deployment re-parameterizes text embeddings for zero runtime text-encoder cost.",content_truncated:!1,metadata:ke({arxiv_id:"2401.17270",authors:[{name:"T. Cheng"},{name:"L. Song"},{name:"Y. Ge"}],published:"2024-01-30",conference:"CVPR 2024",citation_count:890,tasks:[{name:"Open-Vocabulary Object Detection",slug:"open-vocabulary-object-detection"},Ie],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:ke({source:"external",source_url:"https://docs.ultralytics.com/models/",authors:[{name:"Ultralytics"}],published:"2025-01-02",tasks:[Ie,Le],repositories:[{url:"https://github.com/ultralytics/ultralytics",stars:46e3}],project_pages:[{url:"https://docs.ultralytics.com"}]})}},tr=t=>t.map(e=>is[e]),Ft=tr(["2502.12524","2407.17140","2410.13842","2406.03459","2304.08069","2405.14458","2401.17270","158342"]),qt=tr(["2410.13842","2502.12524","2407.17140","2405.14458","2304.08069"]),Pt="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.",Yt="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.",Bt=`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.`,os=["How do these compare on edge devices like Jetson?","Show me open-vocabulary detectors","What about small-object detection?"];async function*cs(t,e){const r=()=>(e==null?void 0:e.aborted)===!0,s=t.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 me(350,e),!r()){yield{type:"reasoning_start"};for(let a=0;a<Pt.length;a+=6){if(await me(14,e),r())return;yield{type:"reasoning_delta",text:Pt.slice(a,a+6)}}if(await me(400,e),!r()&&(yield{type:"reasoning_end"},await me(300,e),!r()&&(yield{type:"tool_call",id:"call_1",name:"search_papers",args:{query:s,top_k:8}},await me(1100,e),!r()&&(yield{type:"tool_result",id:"call_1",hits:Ft.length,took_ms:214,results:Ft},await me(350,e),!r())))){yield{type:"reasoning_start"};for(let a=0;a<Yt.length;a+=6){if(await me(14,e),r())return;yield{type:"reasoning_delta",text:Yt.slice(a,a+6)}}if(await me(350,e),!r()&&(yield{type:"reasoning_end"},await me(300,e),!r()&&(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 me(900,e),!r()&&(yield{type:"tool_result",id:"call_2",hits:qt.length,took_ms:176,results:qt},await me(400,e),!r())))){for(let a=0;a<Bt.length;a+=4){if(await me(11,e),r())return;yield{type:"answer_delta",text:Bt.slice(a,a+4)}}yield{type:"done",followups:os,finish_reason:"success"}}}}}const Pe=()=>{const e=String("0").trim().toLowerCase();return e==="1"||e==="true"||e==="yes"},ls=()=>!0;class Ue extends Error{constructor(e,r,s,a){super(e),this.code=r,this.status=s,this.retryAfterSeconds=a,this.name="ChatStreamError"}}class nt extends Error{constructor(e="The assistant stream was interrupted."){super(e),this.name="ChatTransportError"}}function us(t,e){if(Pe())try{qe.reportLatency(t,e).catch(()=>{})}catch{}}function ds(t,e={}){return Pe()?fs(t,e):cs(t.message,e.signal)}function rr(t,e={}){return ps(t,e)}async function*fs(t,e){try{const r=await qe.stream(t,e.signal);yield*nr(r,e)}catch(r){throw sr(r)}}async function*ps(t,e){try{const r=await qe.resumeStream(t,e.signal,e.afterSequence);yield*nr(r,e)}catch(r){throw sr(r)}}function sr(t){return t instanceof Ue||t instanceof nt?t:t instanceof SyntaxError?new Ue("The assistant returned an invalid stream event.","invalid_stream_event"):new nt(t instanceof Error?t.message:"The assistant stream was interrupted.")}function hs(t){let e=null,r="message";const s=[];for(const a of t.split(` | |
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