You can deploy this model and start earning money today!

atalaydenknalbant/
Yolov13

Name: Yolov13
Availability: InStock

Views

No views yet

YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception

Abstract

The YOLO series models reign supreme in real-time object detection due to their superior accuracy and computational efficiency. However, both the convolutional architectures of YOLO11 and earlier versions and the area-based self-attention mechanism introduced in YOLOv12 are limited to local information aggregation and pairwise correlation modeling, lacking the capability to capture global multi-to-multi high-order correlations, which limits detection performance in complex scenarios. In this paper, we propose YOLOv13, an accurate and lightweight object detector. To address the above-mentioned challenges, we propose a Hypergraph-based Adaptive Correlation Enhancement (HyperACE) mechanism that adaptively exploits latent high-order correlations and overcomes the limitation of previous methods that are restricted to pairwise correlation modeling based on hypergraph computation, achieving efficient global cross-location and cross-scale feature fusion and enhancement. Subsequently, we propose a Full-Pipeline Aggregation-and-Distribution (FullPAD) paradigm based on HyperACE, which effectively achieves fine-grained information flow and representation synergy within the entire network by distributing correlation-enhanced features to the full pipeline. Finally, we propose to leverage depthwise separable convolutions to replace vanilla large-kernel convolutions, and design a series of blocks that significantly reduce parameters and computational complexity without sacrificing performance. We conduct extensive experiments on the widely used MS COCO benchmark, and the experimental results demonstrate that our method achieves state-of-the-art performance with fewer parameters and FLOPs. Specifically, our YOLOv13-N improves mAP by 3.0% over YOLO11-N and by 1.5% over YOLOv12-N. The code and models of our YOLOv13 model are available at: this https URL .

Updates

2025-07-19: HuggingFace Spaces Demo is online. Thanks to Atalay!
2025-06-27: Converting YOLOv13 to Huawei Ascend (OM), Rockchip (RKNN) formats is supported. Thanks to kaylorchen!
2025-06-25: FastAPI REST API is supported. Thanks to MohibShaikh!
2025-06-24: 🔥 The paper of YOLOv13 can be downloaded: 🔗 YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception.
2025-06-24: Android deployment is supported. Thanks to mpj1234!
2025-06-22: YOLOv13 model weights released.
2025-06-21: The code of YOLOv13 has been open-sourced.

Technical Briefing 💡

Introducing YOLOv13—the next-generation real-time detector with cutting-edge performance and efficiency. YOLOv13 family includes four variants: Nano, Small, Large, and X-Large, powered by:

HyperACE: Hypergraph-based Adaptive Correlation Enhancement
- Treats pixels in multi-scale feature maps as hypergraph vertices.
- Adopts a learnable hyperedge construction module to adaptively exploring high-order correlations between vertices.
- A message passing module with linear complexity is leveraged to effectively aggregate multi-scale features with the guidance of high-order correlations to achieve effective visual perception of complex scenarios.
FullPAD: Full-Pipeline Aggregation-and-Distribution Paradigm
- Uses the HyperACE to aggregate multi-scale features of the backbone and extract high-order correlations in the hypergraph space.
- FullPAD paradigm further leverages three separate tunnels to forward these correlation-enhanced features to the connection between the backbone and neck, the internal layers of the neck, and the connection between the neck and head, respectively. In this way, YOLOv13 achieves fine‑grained information flow and representational synergy across the entire pipeline.
- FullPAD significantly improves gradient propagation and enhances the detection performance.
Model Lightweighting via DS-based Blocks
- Replaces large-kernel convolutions with blocks building based on depthwise separable convolutions (DSConv, DS-Bottleneck, DS-C3k, DS-C3k2), preserving receptive field while greatly reducing parameters and computation.
- Achieves faster inference speed without sacrificing accuracy.

YOLOv13 seamlessly combines hypergraph computation with end-to-end information collaboration to deliver a more accurate, robust, and efficient real-time detection solution.

Main Results 🏆

1. MS COCO Benchmark

Table 1. Quantitative comparison with other state-of-the-art real-time object detectors on the MS COCO dataset

Method	FLOPs (G)	Parameters(M)	AP~~50:95~~^val^	AP50^val^	AP75^val^	Latency (ms)
YOLOv6-3.0-N	11.4	4.7	37.0	52.7	–	2.74
Gold-YOLO-N	12.1	5.6	39.6	55.7	–	2.97
YOLOv8-N	8.7	3.2	37.4	52.6	40.5	1.77
YOLOv10-N	6.7	2.3	38.5	53.8	41.7	1.84
YOLO11-N	6.5	2.6	38.6	54.2	41.6	1.53
YOLOv12-N	6.5	2.6	40.1	56.0	43.4	1.83
YOLOv13-N	6.4	2.5	41.6	57.8	45.1	1.97

YOLOv6-3.0-S	45.3	18.5	44.3	61.2	–	3.42
Gold-YOLO-S	46.0	21.5	45.4	62.5	–	3.82
YOLOv8-S	28.6	11.2	45.0	61.8	48.7	2.33
RT-DETR-R18	60.0	20.0	46.5	63.8	–	4.58
RT-DETRv2-R18	60.0	20.0	47.9	64.9	–	4.58
YOLOv9-S	26.4	7.1	46.8	63.4	50.7	3.44
YOLOv10-S	21.6	7.2	46.3	63.0	50.4	2.53
YOLO11-S	21.5	9.4	45.8	62.6	49.8	2.56
YOLOv12-S	21.4	9.3	47.1	64.2	51.0	2.82
YOLOv13-S	20.8	9.0	48.0	65.2	52.0	2.98

YOLOv6-3.0-L	150.7	59.6	51.8	69.2	–	9.01
Gold-YOLO-L	151.7	75.1	51.8	68.9	–	10.69
YOLOv8-L	165.2	43.7	53.0	69.8	57.7	8.13
RT-DETR-R50	136.0	42.0	53.1	71.3	–	6.93
RT-DETRv2-R50	136.0	42.0	53.4	71.6	–	6.93
YOLOv9-C	102.1	25.3	53.0	70.2	57.8	6.64
YOLOv10-L	120.3	24.4	53.2	70.1	57.2	7.31
YOLO11-L	86.9	25.3	52.3	69.2	55.7	6.23
YOLOv12-L	88.9	26.4	53.0	70.0	57.9	7.10
YOLOv13-L	88.4	27.6	53.4	70.9	58.1	8.63

YOLOv8-X	257.8	68.2	54.0	71.0	58.8	12.83
RT-DETR-R101	259.0	76.0	54.3	72.7	–	13.51
RT-DETRv2-R101	259.0	76.0	54.3	72.8	–	13.51
YOLOv10-X	160.4	29.5	54.4	71.3	59.3	10.70
YOLO11-X	194.9	56.9	54.2	71.0	59.1	11.35
YOLOv12-X	199.0	59.1	54.4	71.1	59.3	12.46
YOLOv13-X	199.2	64.0	54.8	72.0	59.8	14.67

2. Visualizations

Visualization examples of YOLOv10-N/S, YOLO11-N/S, YOLOv12-N/S, and YOLOv13-N/S.

Representative visualization examples of adaptive hyperedges. The hyperedges in the first and second columns mainly focus on the high-order interactions among objects in the foreground. The third column mainly focuses on the high-order interactions between the background and part of the foreground. The visualization of these hyperedges can intuitively reflect the high-order visual associations modeled by the YOLOv13.

Quick Start 🚀

1. Install Dependencies

bash

wget https://github.com/Dao-AILab/flash-attention/releases/download/v2.7.3/flash_attn-2.7.3+cu11torch2.2cxx11abiFALSE-cp311-cp311-linux_x86_64.whl
conda create -n yolov13 python=3.11
conda activate yolov13
pip install -r requirements.txt
pip install -e .

YOLOv13 suppports Flash Attention acceleration.

2. Validation

YOLOv13-N YOLOv13-S YOLOv13-L YOLOv13-X

Use the following code to validate the YOLOv13 models on the COCO dataset. Make sure to replace {n/s/l/x} with the desired model scale (nano, small, plus, or ultra).

python

from ultralytics import YOLO

model = YOLO('yolov13{n/s/l/x}.pt')  # Replace with the desired model scale

3. Training

Use the following code to train the YOLOv13 models. Make sure to replace yolov13n.yaml with the desired model configuration file path, and coco.yaml with your coco dataset configuration file.

python

from ultralytics import YOLO

model = YOLO('yolov13n.yaml')

# Train the model
results = model.train(
  data='coco.yaml',
  epochs=600, 
  batch=256, 
  imgsz=640,
  scale=0.5,  # S:0.9; L:0.9; X:0.9
  mosaic=1.0,
  mixup=0.0,  # S:0.05; L:0.15; X:0.2
  copy_paste=0.1,  # S:0.15; L:0.5; X:0.6
  device="0,1,2,3",
)

# Evaluate model performance on the validation set
metrics = model.val('coco.yaml')

# Perform object detection on an image
results = model("path/to/your/image.jpg")
results[0].show()

4. Prediction

Use the following code to perform object detection using the YOLOv13 models. Make sure to replace {n/s/l/x} with the desired model scale.

python

from ultralytics import YOLO

model = YOLO('yolov13{n/s/l/x}.pt')  # Replace with the desired model scale
model.predict()

5. Export

Use the following code to export the YOLOv13 models to ONNX or TensorRT format. Make sure to replace {n/s/l/x} with the desired model scale.

python

from ultralytics import YOLO
model = YOLO('yolov13{n/s/l/x}.pt')  # Replace with the desired model scale
model.export(format="engine", half=True)  # or format="onnx"

Related Projects 🔗

The code is based on Ultralytics. Thanks for their excellent work!
Other wonderful works about Hypergraph Computation:
- "Hypergraph Neural Networks": [paper] [code]
- "HGNN+: General Hypergraph Nerual Networks": [paper] [code]
- "SoftHGNN: Soft Hypergraph Neural Networks for General Visual Recognition": [paper] [code]

Citation 📝

bibtex

@article{yolov13,
  title={YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception},
  author={Lei, Mengqi and Li, Siqi and Wu, Yihong and et al.},
  journal={arXiv preprint arXiv:2506.17733},
  year={2025}
}

atalaydenknalbant/Yolov13

YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception

YOLOv13: Real-Time Object Detection with Hypergraph-Enhanced Adaptive Visual Perception

Abstract

Updates

Technical Briefing 💡

Main Results 🏆

1. MS COCO Benchmark

2. Visualizations

Quick Start 🚀

1. Install Dependencies

2. Validation

3. Training

4. Prediction

5. Export

Related Projects 🔗

Citation 📝

atalaydenknalbant/
Yolov13