HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.0.151 add DOTAv2.yaml for OBB training (#4258)

Browse source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2023-08-10 00:55:36 +02:00 • committed by GitHub
parent a76af55533
commit c9be1f3cce
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
46 changed files with 805 additions and 303 deletions
Download patch file Download diff file Expand all files Collapse all files

24
docs/datasets/detect/objects365.md
View file

@ -51,7 +51,7 @@ To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image
model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training)
# Train the model
model.train(data='Objects365.yaml', epochs=100, imgsz=640)
results = model.train(data='Objects365.yaml', epochs=100, imgsz=640)
```
=== "CLI"
@ -75,14 +75,18 @@ The example showcases the variety and complexity of the data in the Objects365 d
If you use the Objects365 dataset in your research or development work, please cite the following paper:
```bibtex
@inproceedings{shao2019objects365,
title={Objects365: A Large-scale, High-quality Dataset for Object Detection},
author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Li, Jing and Zhang, Xiangyu and Sun, Jian},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={8425--8434},
year={2019}
}
```
!!! note ""
=== "BibTeX"
```bibtex
@inproceedings{shao2019objects365,
title={Objects365: A Large-scale, High-quality Dataset for Object Detection},
author={Shao, Shuai and Li, Zeming and Zhang, Tianyuan and Peng, Chao and Yu, Gang and Li, Jing and Zhang, Xiangyu and Sun, Jian},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={8425--8434},
year={2019}
}
```
We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).