# [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458)
Official PyTorch implementation of **YOLOv10**.
<p align="center">
<img src="figures/latency.svg" width=48%>
<img src="figures/params.svg" width=48%> <br>
Comparisons with others in terms of latency-accuracy (left) and size-accuracy (right) trade-offs.
</p>
[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458).\
Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding\
[](https://arxiv.org/abs/2405.14458) <a href="https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a> [](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) [](https://huggingface.co/spaces/jameslahm/YOLOv10) [](https://huggingface.co/spaces/kadirnar/Yolov10) [](https://huggingface.co/spaces/Xenova/yolov10-web) [](https://learnopencv.com/yolov10/) [](https://openbayes.com/console/public/tutorials/im29uYrnIoz)
<details>
<summary>
<font size="+1">Abstract</font>
</summary>
Over the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both the efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves the state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8$\times$ faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8$\times$ smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\% less latency and 25\% fewer parameters for the same performance.
</details>
## Notes
- 2024/05/31: Please use the [exported format](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export) for benchmark. In the non-exported format, e.g., pytorch, the speed of YOLOv10 is biased because the unnecessary `cv2` and `cv3` operations in the `v10Detect` are executed during inference.
- 2024/05/30: We provide [some clarifications and suggestions](https://github.com/THU-MIG/yolov10/issues/136) for detecting smaller objects or objects in the distance with YOLOv10. Thanks to [SkalskiP](https://github.com/SkalskiP)!
- 2024/05/27: We have updated the [checkpoints](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) with class names, for ease of use.
## UPDATES 🔥
- 2024/06/01: Thanks to [ErlanggaYudiPradana](https://github.com/rlggyp) for the integration with [C++ | OpenVINO | OpenCV](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference)
- 2024/06/01: Thanks to [NielsRogge](https://github.com/NielsRogge) and [AK](https://x.com/_akhaliq) for hosting the models on the HuggingFace Hub!
- 2024/05/31: Build [yolov10-jetson](https://github.com/Seeed-Projects/jetson-examples/blob/main/reComputer/scripts/yolov10/README.md) docker image by [youjiang](https://github.com/yuyoujiang)!
- 2024/05/31: Thanks to [mohamedsamirx](https://github.com/mohamedsamirx) for the integration with [BoTSORT, DeepOCSORT, OCSORT, HybridSORT, ByteTrack, StrongSORT using BoxMOT library](https://colab.research.google.com/drive/1-QV2TNfqaMsh14w5VxieEyanugVBG14V?usp=sharing)!
- 2024/05/31: Thanks to [kaylorchen](https://github.com/kaylorchen) for the integration with [rk3588](https://github.com/kaylorchen/rk3588-yolo-demo)!
- 2024/05/30: Thanks to [eaidova](https://github.com/eaidova) for the integration with [OpenVINOâ„¢](https://github.com/openvinotoolkit/openvino_notebooks/blob/0ba3c0211bcd49aa860369feddffdf7273a73c64/notebooks/yolov10-optimization/yolov10-optimization.ipynb)!
- 2024/05/29: Add the gradio demo for running the models locally. Thanks to [AK](https://x.com/_akhaliq)!
- 2024/05/27: Thanks to [sujanshresstha](sujanshresstha) for the integration with [DeepSORT](https://github.com/sujanshresstha/YOLOv10_DeepSORT.git)!
- 2024/05/26: Thanks to [CVHub520](https://github.com/CVHub520) for the integration into [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling)!
- 2024/05/26: Thanks to [DanielSarmiento04](https://github.com/DanielSarmiento04) for integrate in [c++ | ONNX | OPENCV](https://github.com/DanielSarmiento04/yolov10cpp)!
- 2024/05/25: Add [Transformers.js demo](https://huggingface.co/spaces/Xenova/yolov10-web) and onnx weights(yolov10[n](https://huggingface.co/onnx-community/yolov10n)/[s](https://huggingface.co/onnx-community/yolov10s)/[m](https://huggingface.co/onnx-community/yolov10m)/[b](https://huggingface.co/onnx-community/yolov10b)/[l](https://huggingface.co/onnx-community/yolov10l)/[x](https://huggingface.co/onnx-community/yolov10x)). Thanks to [xenova](https://github.com/xenova)!
- 2024/05/25: Add [colab demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s), [HuggingFace Demo](https://huggingface.co/spaces/kadirnar/Yolov10), and [HuggingFace Model Page](https://huggingface.co/kadirnar/Yolov10). Thanks to [SkalskiP](https://github.com/SkalskiP) and [kadirnar](https://github.com/kadirnar)!
## Performance
COCO
| Model | Test Size | #Params | FLOPs | AP<sup>val</sup> | Latency |
|:---------------|:----:|:---:|:--:|:--:|:--:|
| [YOLOv10-N](https://huggingface.co/jameslahm/yolov10n) | 640 | 2.3M | 6.7G | 38.5% | 1.84ms |
| [YOLOv10-S](https://huggingface.co/jameslahm/yolov10s) | 640 | 7.2M | 21.6G | 46.3% | 2.49ms |
| [YOLOv10-M](https://huggingface.co/jameslahm/yolov10m) | 640 | 15.4M | 59.1G | 51.1% | 4.74ms |
| [YOLOv10-B](https://huggingface.co/jameslahm/yolov10b) | 640 | 19.1M | 92.0G | 52.5% | 5.74ms |
| [YOLOv10-L](https://huggingface.co/jameslahm/yolov10l) | 640 | 24.4M | 120.3G | 53.2% | 7.28ms |
| [YOLOv10-X](https://huggingface.co/jameslahm/yolov10x) | 640 | 29.5M | 160.4G | 54.4% | 10.70ms |
## Installation
`conda` virtual environment is recommended.
```
conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
pip install -e .
```
## Demo
```
python app.py
# Please visit http://127.0.0.1:7860
```
## Validation
[`yolov10n`](https://huggingface.co/jameslahm/yolov10n) [`yolov10s`](https://huggingface.co/jameslahm/yolov10s) [`yolov10m`](https://huggingface.co/jameslahm/yolov10m) [`yolov10b`](https://huggingface.co/jameslahm/yolov10b) [`yolov10l`](https://huggingface.co/jameslahm/yolov10l) [`yolov10x`](https://huggingface.co/jameslahm/yolov10x)
```
yolo val model=jameslahm/yolov10{n/s/m/b/l/x} data=coco.yaml batch=256
```
Or
```python
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model.val(data='coco.yaml', batch=256)
```
## Training
```
yolo detect train data=coco.yaml model=yolov10n/s/m/b/l/x.yaml epochs=500 batch=256 imgsz=640 device=0,1,2,3,4,5,6,7
```
Or
```python
from ultralytics import YOLOv10
model = YOLOv10()
# If you want to finetune the model with pretrained weights, you could load the
# pretrained weights like below
# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model.train(data='coco.yaml', epochs=500, batch=256, imgsz=640)
```
## Push to hub to 🤗
Optionally, you can push your fine-tuned model to the [Hugging Face hub](https://huggingface.co/) as a public or private model:
```python
# let's say you have fine-tuned a model for crop detection
model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-detection")
# you can also pass `private=True` if you don't want everyone to see your model
model.push_to_hub("<your-hf-username-or-organization/yolov10-finetuned-crop-detection", private=True)
```
## Prediction
Note that a smaller confidence threshold can be set to detect smaller objects or objects in the distance. Please refer to [here](https://github.com/THU-MIG/yolov10/issues/136) for details.
```
yolo predict model=jameslahm/yolov10{n/s/m/b/l/x}
```
Or
```python
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model.predict()
```
## Export
```
# End-to-End ONNX
yolo export model=jameslahm/yolov10{n/s/m/b/l/x} format=onnx opset=13 simplify
# Predict with ONNX
yolo predict model=yolov10n/s/m/b/l/x.onnx
# End-to-End TensorRT
yolo export model=jameslahm/yolov10{n/s/m/b/l/x} format=engine half=True simplify opset=13 workspace=16
# or
trtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16
# Predict with TensorRT
yolo predict model=yolov10n/s/m/b/l/x.engine
```
Or
```python
from ultralytics import YOLOv10
model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')
# or
# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt
model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')
model.export(...)
```
## Acknowledgement
The code base is built with [ultralytics](https://github.com/ultralytics/ultralytics) and [RT-DETR](https://github.com/lyuwenyu/RT-DETR).
Thanks for the great implementations!
## Citation
If our code or models help your work, please cite our paper:
```BibTeX
@article{wang2024yolov10,
title={YOLOv10: Real-Time End-to-End Object Detection},
author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},
journal={arXiv preprint arXiv:2405.14458},
year={2024}
}
```
Raw data
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"keywords": "machine-learning, deep-learning, computer-vision, ML, DL, AI, YOLO, YOLOv3, YOLOv5, YOLOv8, HUB, Ultralytics",
"author": "Glenn Jocher, Ayush Chaurasia, Jing Qiu",
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"description": "# [YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458)\r\n\r\n\r\nOfficial PyTorch implementation of **YOLOv10**.\r\n\r\n<p align=\"center\">\r\n <img src=\"figures/latency.svg\" width=48%>\r\n <img src=\"figures/params.svg\" width=48%> <br>\r\n Comparisons with others in terms of latency-accuracy (left) and size-accuracy (right) trade-offs.\r\n</p>\r\n\r\n[YOLOv10: Real-Time End-to-End Object Detection](https://arxiv.org/abs/2405.14458).\\\r\nAo Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding\\\r\n[](https://arxiv.org/abs/2405.14458) <a href=\"https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a> [](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) [](https://huggingface.co/spaces/jameslahm/YOLOv10) [](https://huggingface.co/spaces/kadirnar/Yolov10) [](https://huggingface.co/spaces/Xenova/yolov10-web) [](https://learnopencv.com/yolov10/) [](https://openbayes.com/console/public/tutorials/im29uYrnIoz) \r\n\r\n\r\n<details>\r\n <summary>\r\n <font size=\"+1\">Abstract</font>\r\n </summary>\r\nOver the past years, YOLOs have emerged as the predominant paradigm in the field of real-time object detection owing to their effective balance between computational cost and detection performance. Researchers have explored the architectural designs, optimization objectives, data augmentation strategies, and others for YOLOs, achieving notable progress. However, the reliance on the non-maximum suppression (NMS) for post-processing hampers the end-to-end deployment of YOLOs and adversely impacts the inference latency. Besides, the design of various components in YOLOs lacks the comprehensive and thorough inspection, resulting in noticeable computational redundancy and limiting the model's capability. It renders the suboptimal efficiency, along with considerable potential for performance improvements. In this work, we aim to further advance the performance-efficiency boundary of YOLOs from both the post-processing and the model architecture. To this end, we first present the consistent dual assignments for NMS-free training of YOLOs, which brings the competitive performance and low inference latency simultaneously. Moreover, we introduce the holistic efficiency-accuracy driven model design strategy for YOLOs. We comprehensively optimize various components of YOLOs from both the efficiency and accuracy perspectives, which greatly reduces the computational overhead and enhances the capability. The outcome of our effort is a new generation of YOLO series for real-time end-to-end object detection, dubbed YOLOv10. Extensive experiments show that YOLOv10 achieves the state-of-the-art performance and efficiency across various model scales. For example, our YOLOv10-S is 1.8$\\times$ faster than RT-DETR-R18 under the similar AP on COCO, meanwhile enjoying 2.8$\\times$ smaller number of parameters and FLOPs. Compared with YOLOv9-C, YOLOv10-B has 46\\% less latency and 25\\% fewer parameters for the same performance.\r\n</details>\r\n\r\n## Notes\r\n- 2024/05/31: Please use the [exported format](https://github.com/THU-MIG/yolov10?tab=readme-ov-file#export) for benchmark. In the non-exported format, e.g., pytorch, the speed of YOLOv10 is biased because the unnecessary `cv2` and `cv3` operations in the `v10Detect` are executed during inference.\r\n- 2024/05/30: We provide [some clarifications and suggestions](https://github.com/THU-MIG/yolov10/issues/136) for detecting smaller objects or objects in the distance with YOLOv10. Thanks to [SkalskiP](https://github.com/SkalskiP)!\r\n- 2024/05/27: We have updated the [checkpoints](https://huggingface.co/collections/jameslahm/yolov10-665b0d90b0b5bb85129460c2) with class names, for ease of use.\r\n\r\n## UPDATES \ud83d\udd25\r\n- 2024/06/01: Thanks to [ErlanggaYudiPradana](https://github.com/rlggyp) for the integration with [C++ | OpenVINO | OpenCV](https://github.com/rlggyp/YOLOv10-OpenVINO-CPP-Inference)\r\n- 2024/06/01: Thanks to [NielsRogge](https://github.com/NielsRogge) and [AK](https://x.com/_akhaliq) for hosting the models on the HuggingFace Hub!\r\n- 2024/05/31: Build [yolov10-jetson](https://github.com/Seeed-Projects/jetson-examples/blob/main/reComputer/scripts/yolov10/README.md) docker image by [youjiang](https://github.com/yuyoujiang)!\r\n- 2024/05/31: Thanks to [mohamedsamirx](https://github.com/mohamedsamirx) for the integration with [BoTSORT, DeepOCSORT, OCSORT, HybridSORT, ByteTrack, StrongSORT using BoxMOT library](https://colab.research.google.com/drive/1-QV2TNfqaMsh14w5VxieEyanugVBG14V?usp=sharing)!\r\n- 2024/05/31: Thanks to [kaylorchen](https://github.com/kaylorchen) for the integration with [rk3588](https://github.com/kaylorchen/rk3588-yolo-demo)!\r\n- 2024/05/30: Thanks to [eaidova](https://github.com/eaidova) for the integration with [OpenVINO\u2122](https://github.com/openvinotoolkit/openvino_notebooks/blob/0ba3c0211bcd49aa860369feddffdf7273a73c64/notebooks/yolov10-optimization/yolov10-optimization.ipynb)!\r\n- 2024/05/29: Add the gradio demo for running the models locally. Thanks to [AK](https://x.com/_akhaliq)!\r\n- 2024/05/27: Thanks to [sujanshresstha](sujanshresstha) for the integration with [DeepSORT](https://github.com/sujanshresstha/YOLOv10_DeepSORT.git)!\r\n- 2024/05/26: Thanks to [CVHub520](https://github.com/CVHub520) for the integration into [X-AnyLabeling](https://github.com/CVHub520/X-AnyLabeling)!\r\n- 2024/05/26: Thanks to [DanielSarmiento04](https://github.com/DanielSarmiento04) for integrate in [c++ | ONNX | OPENCV](https://github.com/DanielSarmiento04/yolov10cpp)!\r\n- 2024/05/25: Add [Transformers.js demo](https://huggingface.co/spaces/Xenova/yolov10-web) and onnx weights(yolov10[n](https://huggingface.co/onnx-community/yolov10n)/[s](https://huggingface.co/onnx-community/yolov10s)/[m](https://huggingface.co/onnx-community/yolov10m)/[b](https://huggingface.co/onnx-community/yolov10b)/[l](https://huggingface.co/onnx-community/yolov10l)/[x](https://huggingface.co/onnx-community/yolov10x)). Thanks to [xenova](https://github.com/xenova)!\r\n- 2024/05/25: Add [colab demo](https://colab.research.google.com/github/roboflow-ai/notebooks/blob/main/notebooks/train-yolov10-object-detection-on-custom-dataset.ipynb#scrollTo=SaKTSzSWnG7s), [HuggingFace Demo](https://huggingface.co/spaces/kadirnar/Yolov10), and [HuggingFace Model Page](https://huggingface.co/kadirnar/Yolov10). Thanks to [SkalskiP](https://github.com/SkalskiP) and [kadirnar](https://github.com/kadirnar)! \r\n\r\n## Performance\r\nCOCO\r\n\r\n| Model | Test Size | #Params | FLOPs | AP<sup>val</sup> | Latency |\r\n|:---------------|:----:|:---:|:--:|:--:|:--:|\r\n| [YOLOv10-N](https://huggingface.co/jameslahm/yolov10n) | 640 | 2.3M | 6.7G | 38.5% | 1.84ms |\r\n| [YOLOv10-S](https://huggingface.co/jameslahm/yolov10s) | 640 | 7.2M | 21.6G | 46.3% | 2.49ms |\r\n| [YOLOv10-M](https://huggingface.co/jameslahm/yolov10m) | 640 | 15.4M | 59.1G | 51.1% | 4.74ms |\r\n| [YOLOv10-B](https://huggingface.co/jameslahm/yolov10b) | 640 | 19.1M | 92.0G | 52.5% | 5.74ms |\r\n| [YOLOv10-L](https://huggingface.co/jameslahm/yolov10l) | 640 | 24.4M | 120.3G | 53.2% | 7.28ms |\r\n| [YOLOv10-X](https://huggingface.co/jameslahm/yolov10x) | 640 | 29.5M | 160.4G | 54.4% | 10.70ms |\r\n\r\n## Installation\r\n`conda` virtual environment is recommended. \r\n```\r\nconda create -n yolov10 python=3.9\r\nconda activate yolov10\r\npip install -r requirements.txt\r\npip install -e .\r\n```\r\n## Demo\r\n```\r\npython app.py\r\n# Please visit http://127.0.0.1:7860\r\n```\r\n\r\n## Validation\r\n[`yolov10n`](https://huggingface.co/jameslahm/yolov10n) [`yolov10s`](https://huggingface.co/jameslahm/yolov10s) [`yolov10m`](https://huggingface.co/jameslahm/yolov10m) [`yolov10b`](https://huggingface.co/jameslahm/yolov10b) [`yolov10l`](https://huggingface.co/jameslahm/yolov10l) [`yolov10x`](https://huggingface.co/jameslahm/yolov10x) \r\n```\r\nyolo val model=jameslahm/yolov10{n/s/m/b/l/x} data=coco.yaml batch=256\r\n```\r\n\r\nOr\r\n```python\r\nfrom ultralytics import YOLOv10\r\n\r\nmodel = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')\r\n# or\r\n# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt\r\nmodel = YOLOv10('yolov10{n/s/m/b/l/x}.pt')\r\n\r\nmodel.val(data='coco.yaml', batch=256)\r\n```\r\n\r\n\r\n## Training \r\n```\r\nyolo detect train data=coco.yaml model=yolov10n/s/m/b/l/x.yaml epochs=500 batch=256 imgsz=640 device=0,1,2,3,4,5,6,7\r\n```\r\n\r\nOr\r\n```python\r\nfrom ultralytics import YOLOv10\r\n\r\nmodel = YOLOv10()\r\n# If you want to finetune the model with pretrained weights, you could load the \r\n# pretrained weights like below\r\n# model = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')\r\n# or\r\n# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt\r\n# model = YOLOv10('yolov10{n/s/m/b/l/x}.pt')\r\n\r\nmodel.train(data='coco.yaml', epochs=500, batch=256, imgsz=640)\r\n```\r\n\r\n## Push to hub to \ud83e\udd17\r\n\r\nOptionally, you can push your fine-tuned model to the [Hugging Face hub](https://huggingface.co/) as a public or private model:\r\n\r\n```python\r\n# let's say you have fine-tuned a model for crop detection\r\nmodel.push_to_hub(\"<your-hf-username-or-organization/yolov10-finetuned-crop-detection\")\r\n\r\n# you can also pass `private=True` if you don't want everyone to see your model\r\nmodel.push_to_hub(\"<your-hf-username-or-organization/yolov10-finetuned-crop-detection\", private=True)\r\n```\r\n\r\n## Prediction\r\nNote that a smaller confidence threshold can be set to detect smaller objects or objects in the distance. Please refer to [here](https://github.com/THU-MIG/yolov10/issues/136) for details.\r\n```\r\nyolo predict model=jameslahm/yolov10{n/s/m/b/l/x}\r\n```\r\n\r\nOr\r\n```python\r\nfrom ultralytics import YOLOv10\r\n\r\nmodel = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')\r\n# or\r\n# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt\r\nmodel = YOLOv10('yolov10{n/s/m/b/l/x}.pt')\r\n\r\nmodel.predict()\r\n```\r\n\r\n## Export\r\n```\r\n# End-to-End ONNX\r\nyolo export model=jameslahm/yolov10{n/s/m/b/l/x} format=onnx opset=13 simplify\r\n# Predict with ONNX\r\nyolo predict model=yolov10n/s/m/b/l/x.onnx\r\n\r\n# End-to-End TensorRT\r\nyolo export model=jameslahm/yolov10{n/s/m/b/l/x} format=engine half=True simplify opset=13 workspace=16\r\n# or\r\ntrtexec --onnx=yolov10n/s/m/b/l/x.onnx --saveEngine=yolov10n/s/m/b/l/x.engine --fp16\r\n# Predict with TensorRT\r\nyolo predict model=yolov10n/s/m/b/l/x.engine\r\n```\r\n\r\nOr\r\n```python\r\nfrom ultralytics import YOLOv10\r\n\r\nmodel = YOLOv10.from_pretrained('jameslahm/yolov10{n/s/m/b/l/x}')\r\n# or\r\n# wget https://github.com/THU-MIG/yolov10/releases/download/v1.1/yolov10{n/s/m/b/l/x}.pt\r\nmodel = YOLOv10('yolov10{n/s/m/b/l/x}.pt')\r\n\r\nmodel.export(...)\r\n```\r\n\r\n## Acknowledgement\r\n\r\nThe code base is built with [ultralytics](https://github.com/ultralytics/ultralytics) and [RT-DETR](https://github.com/lyuwenyu/RT-DETR).\r\n\r\nThanks for the great implementations! \r\n\r\n## Citation\r\n\r\nIf our code or models help your work, please cite our paper:\r\n```BibTeX\r\n@article{wang2024yolov10,\r\n title={YOLOv10: Real-Time End-to-End Object Detection},\r\n author={Wang, Ao and Chen, Hui and Liu, Lihao and Chen, Kai and Lin, Zijia and Han, Jungong and Ding, Guiguang},\r\n journal={arXiv preprint arXiv:2405.14458},\r\n year={2024}\r\n}\r\n```\r\n",
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