add yolo v10 and modify pipeline

ultralytics/cfg/datasets/Argoverse.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Argoverse-HD dataset (ring-front-center camera) https://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Documentation: https://docs.ultralytics.com/datasets/detect/argoverse/
 # Example usage: yolo train data=Argoverse.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── Argoverse  ← downloads here (31.5 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/Argoverse  # dataset root dir
-train: Argoverse-1.1/images/train/  # train images (relative to 'path') 39384 images
-val: Argoverse-1.1/images/val/  # val images (relative to 'path') 15062 images
-test: Argoverse-1.1/images/test/  # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+path: ../datasets/Argoverse # dataset root dir
+train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
 
 # Classes
 names:
@@ -24,7 +24,6 @@ names:
   6: traffic_light
   7: stop_sign
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import json
@@ -64,7 +63,9 @@ download: |
   # Download 'https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip' (deprecated S3 link)
   dir = Path(yaml['path'])  # dataset root dir
   urls = ['https://drive.google.com/file/d/1st9qW3BeIwQsnR0t8mRpvbsSWIo16ACi/view?usp=drive_link']
-  download(urls, dir=dir)
+  print("\n\nWARNING: Argoverse dataset MUST be downloaded manually, autodownload will NOT work.")
+  print(f"WARNING: Manually download Argoverse dataset '{urls[0]}' to '{dir}' and re-run your command.\n\n")
+  # download(urls, dir=dir)
 
   # Convert
   annotations_dir = 'Argoverse-HD/annotations/'

ultralytics/cfg/datasets/DOTAv1.5.yaml

@@ -1,18 +1,19 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-# DOTA 2.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
-# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv2.yaml
+# DOTA 1.5 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
+# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.5.yaml
 # parent
 # ├── ultralytics
 # └── datasets
-#     └── dota2  ← downloads here (2GB)
+#     └── dota1.5  ← downloads here (2GB)
 
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/DOTAv2  # dataset root dir
-train: images/train  # train images (relative to 'path') 1411 images
-val: images/val  # val images (relative to 'path') 458 images
-test: images/test  # test images (optional) 937 images
+path: ../datasets/DOTAv1.5 # dataset root dir
+train: images/train # train images (relative to 'path') 1411 images
+val: images/val # val images (relative to 'path') 458 images
+test: images/test # test images (optional) 937 images
 
-# Classes for DOTA 2.0
+# Classes for DOTA 1.5
 names:
   0: plane
   1: ship
@@ -30,8 +31,6 @@ names:
   13: soccer ball field
   14: swimming pool
   15: container crane
-  16: airport
-  17: helipad
 
 # Download script/URL (optional)
-download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv2.zip
+download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv1.5.zip

ultralytics/cfg/datasets/DOTAv1.yaml

@@ -0,0 +1,35 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# DOTA 1.0 dataset https://captain-whu.github.io/DOTA/index.html for object detection in aerial images by Wuhan University
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota-v2/
+# Example usage: yolo train model=yolov8n-obb.pt data=DOTAv1.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota1  ← downloads here (2GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/DOTAv1 # dataset root dir
+train: images/train # train images (relative to 'path') 1411 images
+val: images/val # val images (relative to 'path') 458 images
+test: images/test # test images (optional) 937 images
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/yolov5/releases/download/v1.0/DOTAv1.zip

ultralytics/cfg/datasets/GlobalWheat2020.yaml

@@ -1,14 +1,14 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
+# Global Wheat 2020 dataset https://www.global-wheat.com/ by University of Saskatchewan
+# Documentation: https://docs.ultralytics.com/datasets/detect/globalwheat2020/
 # Example usage: yolo train data=GlobalWheat2020.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── GlobalWheat2020  ← downloads here (7.0 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/GlobalWheat2020  # dataset root dir
+path: ../datasets/GlobalWheat2020 # dataset root dir
 train: # train images (relative to 'path') 3422 images
   - images/arvalis_1
   - images/arvalis_2
@@ -29,7 +29,6 @@ test: # test images (optional) 1276 images
 names:
   0: wheat_head
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   from ultralytics.utils.downloads import download

ultralytics/cfg/datasets/ImageNet.yaml

@@ -1,18 +1,18 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
 # Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Documentation: https://docs.ultralytics.com/datasets/classify/imagenet/
 # Example usage: yolo train task=classify data=imagenet
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── imagenet  ← downloads here (144 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/imagenet  # dataset root dir
-train: train  # train images (relative to 'path') 1281167 images
-val: val  # val images (relative to 'path') 50000 images
-test:  # test images (optional)
+path: ../datasets/imagenet # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
 
 # Classes
 names:
@@ -2020,6 +2020,5 @@ map:
   n13133613: ear
   n15075141: toilet_tissue
 
-
 # Download script/URL (optional)
 download: yolo/data/scripts/get_imagenet.sh

ultralytics/cfg/datasets/Objects365.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # Objects365 dataset https://www.objects365.org/ by Megvii
+# Documentation: https://docs.ultralytics.com/datasets/detect/objects365/
 # Example usage: yolo train data=Objects365.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── Objects365  ← downloads here (712 GB = 367G data + 345G zips)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/Objects365  # dataset root dir
-train: images/train  # train images (relative to 'path') 1742289 images
+path: ../datasets/Objects365 # dataset root dir
+train: images/train # train images (relative to 'path') 1742289 images
 val: images/val # val images (relative to 'path') 80000 images
-test:  # test images (optional)
+test: # test images (optional)
 
 # Classes
 names:
@@ -381,7 +381,6 @@ names:
   363: Curling
   364: Table Tennis
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   from tqdm import tqdm

ultralytics/cfg/datasets/SKU-110K.yaml

@@ -1,23 +1,22 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Documentation: https://docs.ultralytics.com/datasets/detect/sku-110k/
 # Example usage: yolo train data=SKU-110K.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── SKU-110K  ← downloads here (13.6 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/SKU-110K  # dataset root dir
-train: train.txt  # train images (relative to 'path')  8219 images
-val: val.txt  # val images (relative to 'path')  588 images
-test: test.txt  # test images (optional)  2936 images
+path: ../datasets/SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path')  8219 images
+val: val.txt # val images (relative to 'path')  588 images
+test: test.txt # test images (optional)  2936 images
 
 # Classes
 names:
   0: object
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import shutil

ultralytics/cfg/datasets/VOC.yaml

@@ -1,12 +1,12 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Documentation: # Documentation: https://docs.ultralytics.com/datasets/detect/voc/
 # Example usage: yolo train data=VOC.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── VOC  ← downloads here (2.8 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
 path: ../datasets/VOC
 train: # train images (relative to 'path')  16551 images
@@ -42,7 +42,6 @@ names:
   18: train
   19: tvmonitor
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import xml.etree.ElementTree as ET
@@ -81,7 +80,7 @@ download: |
   urls = [f'{url}VOCtrainval_06-Nov-2007.zip',  # 446MB, 5012 images
           f'{url}VOCtest_06-Nov-2007.zip',  # 438MB, 4953 images
           f'{url}VOCtrainval_11-May-2012.zip']  # 1.95GB, 17126 images
-  download(urls, dir=dir / 'images', curl=True, threads=3)
+  download(urls, dir=dir / 'images', curl=True, threads=3, exist_ok=True)  # download and unzip over existing paths (required)
 
   # Convert
   path = dir / 'images/VOCdevkit'

ultralytics/cfg/datasets/VisDrone.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Documentation: https://docs.ultralytics.com/datasets/detect/visdrone/
 # Example usage: yolo train data=VisDrone.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── VisDrone  ← downloads here (2.3 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/VisDrone  # dataset root dir
-train: VisDrone2019-DET-train/images  # train images (relative to 'path')  6471 images
-val: VisDrone2019-DET-val/images  # val images (relative to 'path')  548 images
-test: VisDrone2019-DET-test-dev/images  # test images (optional)  1610 images
+path: ../datasets/VisDrone # dataset root dir
+train: VisDrone2019-DET-train/images # train images (relative to 'path')  6471 images
+val: VisDrone2019-DET-val/images # val images (relative to 'path')  548 images
+test: VisDrone2019-DET-test-dev/images # test images (optional)  1610 images
 
 # Classes
 names:
@@ -26,7 +26,6 @@ names:
   8: bus
   9: motor
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import os

ultralytics/cfg/datasets/african-wildlife.yaml

@@ -0,0 +1,24 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# African-wildlife dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/african-wildlife/
+# Example usage: yolo train data=african-wildlife.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── african-wildlife  ← downloads here (100 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/african-wildlife # dataset root dir
+train: train/images # train images (relative to 'path') 1052 images
+val: valid/images # val images (relative to 'path') 225 images
+test: test/images # test images (relative to 'path') 227 images
+
+# Classes
+names:
+  0: buffalo
+  1: elephant
+  2: rhino
+  3: zebra
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/african-wildlife.zip

ultralytics/cfg/datasets/brain-tumor.yaml

@@ -0,0 +1,22 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Brain-tumor dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/brain-tumor/
+# Example usage: yolo train data=brain-tumor.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── brain-tumor  ← downloads here (4.05 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/brain-tumor # dataset root dir
+train: train/images # train images (relative to 'path') 893 images
+val: valid/images # val images (relative to 'path') 223 images
+test: # test images (relative to 'path')
+
+# Classes
+names:
+  0: negative
+  1: positive
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/brain-tumor.zip

ultralytics/cfg/datasets/carparts-seg.yaml

@@ -0,0 +1,43 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Carparts-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/carparts-seg/
+# Example usage: yolo train data=carparts-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── carparts-seg  ← downloads here (132 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/carparts-seg # dataset root dir
+train: train/images # train images (relative to 'path') 3516 images
+val: valid/images # val images (relative to 'path') 276 images
+test: test/images # test images (relative to 'path') 401 images
+
+# Classes
+names:
+  0: back_bumper
+  1: back_door
+  2: back_glass
+  3: back_left_door
+  4: back_left_light
+  5: back_light
+  6: back_right_door
+  7: back_right_light
+  8: front_bumper
+  9: front_door
+  10: front_glass
+  11: front_left_door
+  12: front_left_light
+  13: front_light
+  14: front_right_door
+  15: front_right_light
+  16: hood
+  17: left_mirror
+  18: object
+  19: right_mirror
+  20: tailgate
+  21: trunk
+  22: wheel
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/carparts-seg.zip

ultralytics/cfg/datasets/coco-pose.yaml

@@ -1,20 +1,20 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-# COCO 2017 dataset http://cocodataset.org by Microsoft
+# COCO 2017 dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco/
 # Example usage: yolo train data=coco-pose.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── coco-pose  ← downloads here (20.1 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco-pose  # dataset root dir
-train: train2017.txt  # train images (relative to 'path') 118287 images
-val: val2017.txt  # val images (relative to 'path') 5000 images
-test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+path: ../datasets/coco-pose # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # val images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
 
 # Keypoints
-kpt_shape: [17, 3]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
 flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
 
 # Classes

ultralytics/cfg/datasets/coco.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-# COCO 2017 dataset http://cocodataset.org by Microsoft
+# COCO 2017 dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
 # Example usage: yolo train data=coco.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── coco  ← downloads here (20.1 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco  # dataset root dir
-train: train2017.txt  # train images (relative to 'path') 118287 images
-val: val2017.txt  # val images (relative to 'path') 5000 images
-test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+path: ../datasets/coco # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # val images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
 
 # Classes
 names:
@@ -96,7 +96,6 @@ names:
   78: hair drier
   79: toothbrush
 
-
 # Download script/URL (optional)
 download: |
   from ultralytics.utils.downloads import download

ultralytics/cfg/datasets/coco128-seg.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/coco/
 # Example usage: yolo train data=coco128.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── coco128-seg  ← downloads here (7 MB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128-seg  # dataset root dir
-train: images/train2017  # train images (relative to 'path') 128 images
-val: images/train2017  # val images (relative to 'path') 128 images
-test:  # test images (optional)
+path: ../datasets/coco128-seg # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
 
 # Classes
 names:
@@ -96,6 +96,5 @@ names:
   78: hair drier
   79: toothbrush
 
-
 # Download script/URL (optional)
 download: https://ultralytics.com/assets/coco128-seg.zip

ultralytics/cfg/datasets/coco128.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco/
 # Example usage: yolo train data=coco128.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── coco128  ← downloads here (7 MB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco128  # dataset root dir
-train: images/train2017  # train images (relative to 'path') 128 images
-val: images/train2017  # val images (relative to 'path') 128 images
-test:  # test images (optional)
+path: ../datasets/coco128 # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
 
 # Classes
 names:
@@ -96,6 +96,5 @@ names:
   78: hair drier
   79: toothbrush
 
-
 # Download script/URL (optional)
 download: https://ultralytics.com/assets/coco128.zip

ultralytics/cfg/datasets/coco8-pose.yaml

@@ -1,20 +1,20 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # COCO8-pose dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco8-pose/
 # Example usage: yolo train data=coco8-pose.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── coco8-pose  ← downloads here (1 MB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco8-pose  # dataset root dir
-train: images/train  # train images (relative to 'path') 4 images
-val: images/val  # val images (relative to 'path') 4 images
-test:  # test images (optional)
+path: ../datasets/coco8-pose # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
 
 # Keypoints
-kpt_shape: [17, 3]  # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
 flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
 
 # Classes

ultralytics/cfg/datasets/coco8-seg.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # COCO8-seg dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/coco8-seg/
 # Example usage: yolo train data=coco8-seg.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── coco8-seg  ← downloads here (1 MB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco8-seg  # dataset root dir
-train: images/train  # train images (relative to 'path') 4 images
-val: images/val  # val images (relative to 'path') 4 images
-test:  # test images (optional)
+path: ../datasets/coco8-seg # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
 
 # Classes
 names:
@@ -96,6 +96,5 @@ names:
   78: hair drier
   79: toothbrush
 
-
 # Download script/URL (optional)
 download: https://ultralytics.com/assets/coco8-seg.zip

ultralytics/cfg/datasets/coco8.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # COCO8 dataset (first 8 images from COCO train2017) by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/detect/coco8/
 # Example usage: yolo train data=coco8.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── coco8  ← downloads here (1 MB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/coco8  # dataset root dir
-train: images/train  # train images (relative to 'path') 4 images
-val: images/val  # val images (relative to 'path') 4 images
-test:  # test images (optional)
+path: ../datasets/coco8 # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+test: # test images (optional)
 
 # Classes
 names:
@@ -96,6 +96,5 @@ names:
   78: hair drier
   79: toothbrush
 
-
 # Download script/URL (optional)
 download: https://ultralytics.com/assets/coco8.zip

ultralytics/cfg/datasets/crack-seg.yaml

@@ -0,0 +1,21 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Crack-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/crack-seg/
+# Example usage: yolo train data=crack-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── crack-seg  ← downloads here (91.2 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/crack-seg # dataset root dir
+train: train/images # train images (relative to 'path') 3717 images
+val: valid/images # val images (relative to 'path') 112 images
+test: test/images # test images (relative to 'path') 200 images
+
+# Classes
+names:
+  0: crack
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/crack-seg.zip

ultralytics/cfg/datasets/dota8.yaml

@@ -0,0 +1,34 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# DOTA8 dataset 8 images from split DOTAv1 dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/obb/dota8/
+# Example usage: yolo train model=yolov8n-obb.pt data=dota8.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── dota8  ← downloads here (1MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/dota8 # dataset root dir
+train: images/train # train images (relative to 'path') 4 images
+val: images/val # val images (relative to 'path') 4 images
+
+# Classes for DOTA 1.0
+names:
+  0: plane
+  1: ship
+  2: storage tank
+  3: baseball diamond
+  4: tennis court
+  5: basketball court
+  6: ground track field
+  7: harbor
+  8: bridge
+  9: large vehicle
+  10: small vehicle
+  11: helicopter
+  12: roundabout
+  13: soccer ball field
+  14: swimming pool
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/yolov5/releases/download/v1.0/dota8.zip

ultralytics/cfg/datasets/open-images-v7.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # Open Images v7 dataset https://storage.googleapis.com/openimages/web/index.html by Google
+# Documentation: https://docs.ultralytics.com/datasets/detect/open-images-v7/
 # Example usage: yolo train data=open-images-v7.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── open-images-v7  ← downloads here (561 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/open-images-v7  # dataset root dir
-train: images/train  # train images (relative to 'path') 1743042 images
-val: images/val  # val images (relative to 'path') 41620 images
-test:  # test images (optional)
+path: ../datasets/open-images-v7 # dataset root dir
+train: images/train # train images (relative to 'path') 1743042 images
+val: images/val # val images (relative to 'path') 41620 images
+test: # test images (optional)
 
 # Classes
 names:
@@ -617,7 +617,6 @@ names:
   599: Zebra
   600: Zucchini
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   from ultralytics.utils import LOGGER, SETTINGS, Path, is_ubuntu, get_ubuntu_version

ultralytics/cfg/datasets/package-seg.yaml

@@ -0,0 +1,21 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Package-seg dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/segment/package-seg/
+# Example usage: yolo train data=package-seg.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── package-seg  ← downloads here (102 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/package-seg # dataset root dir
+train: images/train # train images (relative to 'path') 1920 images
+val: images/val # val images (relative to 'path') 89 images
+test: test/images # test images (relative to 'path') 188 images
+
+# Classes
+names:
+  0: package
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/package-seg.zip

ultralytics/cfg/datasets/tiger-pose.yaml

@@ -0,0 +1,24 @@
+# Ultralytics YOLO 🚀, AGPL-3.0 license
+# Tiger Pose dataset by Ultralytics
+# Documentation: https://docs.ultralytics.com/datasets/pose/tiger-pose/
+# Example usage: yolo train data=tiger-pose.yaml
+# parent
+# ├── ultralytics
+# └── datasets
+#     └── tiger-pose  ← downloads here (75.3 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/tiger-pose # dataset root dir
+train: train # train images (relative to 'path') 210 images
+val: val # val images (relative to 'path') 53 images
+
+# Keypoints
+kpt_shape: [12, 2] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
+flip_idx: [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]
+
+# Classes
+names:
+  0: tiger
+
+# Download script/URL (optional)
+download: https://ultralytics.com/assets/tiger-pose.zip

ultralytics/cfg/datasets/xView.yaml

@@ -1,17 +1,17 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
 # DIUx xView 2018 Challenge https://challenge.xviewdataset.org by U.S. National Geospatial-Intelligence Agency (NGA)
 # --------  DOWNLOAD DATA MANUALLY and jar xf val_images.zip to 'datasets/xView' before running train command!  --------
+# Documentation: https://docs.ultralytics.com/datasets/detect/xview/
 # Example usage: yolo train data=xView.yaml
 # parent
 # ├── ultralytics
 # └── datasets
 #     └── xView  ← downloads here (20.7 GB)
 
-
 # Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
-path: ../datasets/xView  # dataset root dir
-train: images/autosplit_train.txt  # train images (relative to 'path') 90% of 847 train images
-val: images/autosplit_val.txt  # train images (relative to 'path') 10% of 847 train images
+path: ../datasets/xView # dataset root dir
+train: images/autosplit_train.txt # train images (relative to 'path') 90% of 847 train images
+val: images/autosplit_val.txt # train images (relative to 'path') 10% of 847 train images
 
 # Classes
 names:
@@ -76,7 +76,6 @@ names:
   58: Pylon
   59: Tower
 
-
 # Download script/URL (optional) ---------------------------------------------------------------------------------------
 download: |
   import json