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

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>
2023-08-10 00:55:36 +02:00
parent a76af55533
commit c9be1f3cce
46 changed files with 805 additions and 303 deletions
--- a/docs/datasets/detect/argoverse.md
+++ b/docs/datasets/detect/argoverse.md
@ -12,7 +12,6 @@ The [Argoverse](https://www.argoverse.org/) dataset is a collection of data desi

    The Argoverse dataset *.zip file required for training was removed from Amazon S3 after the shutdown of Argo AI by Ford, but we have made it available for manual download on [Google Drive](https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link).

-
 ## Key Features

 - Argoverse contains over 290K labeled 3D object tracks and 5 million object instances across 1,263 distinct scenes.
@ -57,7 +56,7 @@ To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image s
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='Argoverse.yaml', epochs=100, imgsz=640)
+        results = model.train(data='Argoverse.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -81,14 +80,18 @@ The example showcases the variety and complexity of the data in the Argoverse da

 If you use the Argoverse dataset in your research or development work, please cite the following paper:

-```bibtex
-@inproceedings{chang2019argoverse,
-  title={Argoverse: 3D Tracking and Forecasting with Rich Maps},
-  author={Chang, Ming-Fang and Lambert, John and Sangkloy, Patsorn and Singh, Jagjeet and Bak, Slawomir and Hartnett, Andrew and Wang, Dequan and Carr, Peter and Lucey, Simon and Ramanan, Deva and others},
-  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  pages={8748--8757},
-  year={2019}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @inproceedings{chang2019argoverse,
+          title={Argoverse: 3D Tracking and Forecasting with Rich Maps},
+          author={Chang, Ming-Fang and Lambert, John and Sangkloy, Patsorn and Singh, Jagjeet and Bak, Slawomir and Hartnett, Andrew and Wang, Dequan and Carr, Peter and Lucey, Simon and Ramanan, Deva and others},
+          booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+          pages={8748--8757},
+          year={2019}
+        }
+        ```

 We would like to acknowledge Argo AI for creating and maintaining the Argoverse dataset as a valuable resource for the autonomous driving research community. For more information about the Argoverse dataset and its creators, visit the [Argoverse dataset website](https://www.argoverse.org/).
--- a/docs/datasets/detect/coco.md
+++ b/docs/datasets/detect/coco.md
@ -52,7 +52,7 @@ To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size o
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='coco.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -76,15 +76,19 @@ The example showcases the variety and complexity of the images in the COCO datas

 If you use the COCO dataset in your research or development work, please cite the following paper:

-```bibtex
-@misc{lin2015microsoft,
-      title={Microsoft COCO: Common Objects in Context},
-      author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
-      year={2015},
-      eprint={1405.0312},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```

 We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
--- a/docs/datasets/detect/coco8.md
+++ b/docs/datasets/detect/coco8.md
@ -42,7 +42,7 @@ To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -66,15 +66,19 @@ The example showcases the variety and complexity of the images in the COCO8 data

 If you use the COCO dataset in your research or development work, please cite the following paper:

-```bibtex
-@misc{lin2015microsoft,
-      title={Microsoft COCO: Common Objects in Context},
-      author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
-      year={2015},
-      eprint={1405.0312},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lin2015microsoft,
+              title={Microsoft COCO: Common Objects in Context},
+              author={Tsung-Yi Lin and Michael Maire and Serge Belongie and Lubomir Bourdev and Ross Girshick and James Hays and Pietro Perona and Deva Ramanan and C. Lawrence Zitnick and Piotr Dollár},
+              year={2015},
+              eprint={1405.0312},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```

 We would like to acknowledge the COCO Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the COCO dataset and its creators, visit the [COCO dataset website](https://cocodataset.org/#home).
--- a/docs/datasets/detect/globalwheat2020.md
+++ b/docs/datasets/detect/globalwheat2020.md
@ -51,7 +51,7 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='GlobalWheat2020.yaml', epochs=100, imgsz=640)
+        results = model.train(data='GlobalWheat2020.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -75,13 +75,17 @@ The example showcases the variety and complexity of the data in the Global Wheat

 If you use the Global Wheat Head Dataset in your research or development work, please cite the following paper:

-```bibtex
-@article{david2020global,
-         title={Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods},
-         author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul and others},
-         journal={arXiv preprint arXiv:2005.02162},
-         year={2020}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{david2020global,
+                 title={Global Wheat Head Detection (GWHD) Dataset: A Large and Diverse Dataset of High-Resolution RGB-Labelled Images to Develop and Benchmark Wheat Head Detection Methods},
+                 author={David, Etienne and Madec, Simon and Sadeghi-Tehran, Pouria and Aasen, Helge and Zheng, Bangyou and Liu, Shouyang and Kirchgessner, Norbert and Ishikawa, Goro and Nagasawa, Koichi and Badhon, Minhajul and others},
+                 journal={arXiv preprint arXiv:2005.02162},
+                 year={2020}
+        }
+        ```

 We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).
--- a/docs/datasets/detect/index.md
+++ b/docs/datasets/detect/index.md
@ -59,7 +59,7 @@ Here's how you can use these formats to train your model:
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
        ```
    === "CLI"

--- a/docs/datasets/detect/objects365.md
+++ b/docs/datasets/detect/objects365.md
@ -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/).
--- a/docs/datasets/detect/open-images-v7.md
+++ b/docs/datasets/detect/open-images-v7.md
@ -70,7 +70,7 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an im
        model = YOLO('yolov8n.pt')

        # Train the model on the Open Images V7 dataset
-        model.train(data='open-images-v7.yaml', epochs=100, imgsz=640)
+        results = model.train(data='open-images-v7.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -94,13 +94,17 @@ Researchers can gain invaluable insights into the array of computer vision chall

 For those employing Open Images V7 in their work, it's prudent to cite the relevant papers and acknowledge the creators:

-```bibtex
-@article{OpenImages,
-  author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari},
-  title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
-  year = {2020},
-  journal = {IJCV}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @article{OpenImages,
+          author = {Alina Kuznetsova and Hassan Rom and Neil Alldrin and Jasper Uijlings and Ivan Krasin and Jordi Pont-Tuset and Shahab Kamali and Stefan Popov and Matteo Malloci and Alexander Kolesnikov and Tom Duerig and Vittorio Ferrari},
+          title = {The Open Images Dataset V4: Unified image classification, object detection, and visual relationship detection at scale},
+          year = {2020},
+          journal = {IJCV}
+        }
+        ```

 A heartfelt acknowledgment goes out to the Google AI team for creating and maintaining the Open Images V7 dataset. For a deep dive into the dataset and its offerings, navigate to the [official Open Images V7 website](https://storage.googleapis.com/openimages/web/index.html).
--- a/docs/datasets/detect/sku-110k.md
+++ b/docs/datasets/detect/sku-110k.md
@ -53,7 +53,7 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image si
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='SKU-110K.yaml', epochs=100, imgsz=640)
+        results = model.train(data='SKU-110K.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -77,13 +77,17 @@ The example showcases the variety and complexity of the data in the SKU-110k dat

 If you use the SKU-110k dataset in your research or development work, please cite the following paper:

-```bibtex
-@inproceedings{goldman2019dense,
- author    = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
- title     = {Precise Detection in Densely Packed Scenes},
- booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
- year      = {2019}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @inproceedings{goldman2019dense,
+         author    = {Eran Goldman and Roei Herzig and Aviv Eisenschtat and Jacob Goldberger and Tal Hassner},
+         title     = {Precise Detection in Densely Packed Scenes},
+         booktitle = {Proc. Conf. Comput. Vision Pattern Recognition (CVPR)},
+         year      = {2019}
+        }
+        ```

 We would like to acknowledge Eran Goldman et al. for creating and maintaining the SKU-110k dataset as a valuable resource for the computer vision research community. For more information about the SKU-110k dataset and its creators, visit the [SKU-110k dataset GitHub repository](https://github.com/eg4000/SKU110K_CVPR19).
--- a/docs/datasets/detect/visdrone.md
+++ b/docs/datasets/detect/visdrone.md
@ -49,7 +49,7 @@ To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image si
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='VisDrone.yaml', epochs=100, imgsz=640)
+        results = model.train(data='VisDrone.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -73,16 +73,20 @@ The example showcases the variety and complexity of the data in the VisDrone dat

 If you use the VisDrone dataset in your research or development work, please cite the following paper:

-```bibtex
-@ARTICLE{9573394,
-  author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
-  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
-  title={Detection and Tracking Meet Drones Challenge},
-  year={2021},
-  volume={},
-  number={},
-  pages={1-1},
-  doi={10.1109/TPAMI.2021.3119563}}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @ARTICLE{9573394,
+          author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Fan, Heng and Hu, Qinghua and Ling, Haibin},
+          journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
+          title={Detection and Tracking Meet Drones Challenge},
+          year={2021},
+          volume={},
+          number={},
+          pages={1-1},
+          doi={10.1109/TPAMI.2021.3119563}}
+        ```

 We would like to acknowledge the AISKYEYE team at the Lab of Machine Learning and Data Mining, Tianjin University, China, for creating and maintaining the VisDrone dataset as a valuable resource for the drone-based computer vision research community. For more information about the VisDrone dataset and its creators, visit the [VisDrone Dataset GitHub repository](https://github.com/VisDrone/VisDrone-Dataset).
--- a/docs/datasets/detect/voc.md
+++ b/docs/datasets/detect/voc.md
@ -52,7 +52,7 @@ To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='VOC.yaml', epochs=100, imgsz=640)
+        results = model.train(data='VOC.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -77,15 +77,19 @@ The example showcases the variety and complexity of the images in the VOC datase

 If you use the VOC dataset in your research or development work, please cite the following paper:

-```bibtex
-@misc{everingham2010pascal,
-      title={The PASCAL Visual Object Classes (VOC) Challenge},
-      author={Mark Everingham and Luc Van Gool and Christopher K. I. Williams and John Winn and Andrew Zisserman},
-      year={2010},
-      eprint={0909.5206},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{everingham2010pascal,
+              title={The PASCAL Visual Object Classes (VOC) Challenge},
+              author={Mark Everingham and Luc Van Gool and Christopher K. I. Williams and John Winn and Andrew Zisserman},
+              year={2010},
+              eprint={0909.5206},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```

 We would like to acknowledge the PASCAL VOC Consortium for creating and maintaining this valuable resource for the computer vision community. For more information about the VOC dataset and its creators, visit the [PASCAL VOC dataset website](http://host.robots.ox.ac.uk/pascal/VOC/).
--- a/docs/datasets/detect/xview.md
+++ b/docs/datasets/detect/xview.md
@ -55,7 +55,7 @@ To train a model on the xView dataset for 100 epochs with an image size of 640,
        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)

        # Train the model
-        model.train(data='xView.yaml', epochs=100, imgsz=640)
+        results = model.train(data='xView.yaml', epochs=100, imgsz=640)
        ```

    === "CLI"
@ -79,15 +79,19 @@ The example showcases the variety and complexity of the data in the xView datase

 If you use the xView dataset in your research or development work, please cite the following paper:

-```bibtex
-@misc{lam2018xview,
-      title={xView: Objects in Context in Overhead Imagery},
-      author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
-      year={2018},
-      eprint={1802.07856},
-      archivePrefix={arXiv},
-      primaryClass={cs.CV}
-}
-```
+!!! note ""
+
+    === "BibTeX"
+
+        ```bibtex
+        @misc{lam2018xview,
+              title={xView: Objects in Context in Overhead Imagery},
+              author={Darius Lam and Richard Kuzma and Kevin McGee and Samuel Dooley and Michael Laielli and Matthew Klaric and Yaroslav Bulatov and Brendan McCord},
+              year={2018},
+              eprint={1802.07856},
+              archivePrefix={arXiv},
+              primaryClass={cs.CV}
+        }
+        ```

 We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).