Official PyTorch implementation of YOLOv10. NeurIPS 2024.

Comparisons with others in terms of latency-accuracy (left) and size-accuracy (right) trade-offs.

YOLOv10: Real-Time End-to-End Object Detection.
Ao Wang, Hui Chen, Lihao Liu, Kai Chen, Zijia Lin, Jungong Han, and Guiguang Ding

• 2024/05/31: Please use the exported format 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 for detecting smaller objects or objects in the distance with YOLOv10. Thanks to SkalskiP!
• 2024/05/27: We have updated the checkpoints with class names, for ease of use.

• 2024/06/01: Thanks to ErlanggaYudiPradana for the integration with C++ | OpenVINO | OpenCV
• 2024/06/01: Thanks to NielsRogge and AK for hosting the models on the HuggingFace Hub!
• 2024/05/31: Build yolov10-jetson docker image by youjiang!
• 2024/05/31: Thanks to mohamedsamirx for the integration with BoTSORT, DeepOCSORT, OCSORT, HybridSORT, ByteTrack, StrongSORT using BoxMOT library!
• 2024/05/31: Thanks to kaylorchen for the integration with rk3588!
• 2024/05/30: Thanks to eaidova for the integration with OpenVINO™!
• 2024/05/29: Add the gradio demo for running the models locally. Thanks to AK!
• 2024/05/27: Thanks to sujanshresstha for the integration with DeepSORT!
• 2024/05/26: Thanks to CVHub520 for the integration into X-AnyLabeling!
• 2024/05/26: Thanks to DanielSarmiento04 for integrate in c++ | ONNX | OPENCV!
• 2024/05/25: Add Transformers.js demo and onnx weights(yolov10n/s/m/b/l/x). Thanks to xenova!
• 2024/05/25: Add colab demo, HuggingFace Demo, and HuggingFace Model Page. Thanks to SkalskiP and kadirnar!

COCO

conda virtual environment is recommended.

conda create -n yolov10 python=3.9
conda activate yolov10
pip install -r requirements.txt
pip install -e .

python app.py
# Please visit http://127.0.0.1:7860

yolov10n yolov10s yolov10m yolov10b yolov10l yolov10x

yolo val model=jameslahm/yolov10{n/s/m/b/l/x} data=coco.yaml batch=256

Or

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)

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

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)

Optionally, you can push your fine-tuned model to the Hugging Face hub as a public or private model:

# 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)

Note that a smaller confidence threshold can be set to detect smaller objects or objects in the distance. Please refer to here for details.

yolo predict model=jameslahm/yolov10{n/s/m/b/l/x}

Or

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()

# 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

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(...)

The code base is built with ultralytics and RT-DETR.

Thanks for the great implementations!

If our code or models help your work, please cite our paper:

@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}
}