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

ultralytics 8.1.39 add YOLO-World training (#9268)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Laughing 2024-03-31 22:30:17 +08:00 • committed by GitHub
parent 18036908d4
commit e9187c1296
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
34 changed files with 2166 additions and 100 deletions
Download patch file Download diff file Expand all files Collapse all files

1
docs/en/datasets/detect/index.md
View file

@ -74,6 +74,7 @@ Here is a list of the supported datasets and a brief description for each:
- [**Argoverse**](argoverse.md): A collection of sensor data collected from autonomous vehicles. It contains 3D tracking annotations for car objects.
- [**COCO**](coco.md): Common Objects in Context (COCO) is a large-scale object detection, segmentation, and captioning dataset with 80 object categories.
- [**LVIS**](lvis.md): LVIS is a large-scale object detection, segmentation, and captioning dataset with 1203 object categories.
- [**COCO8**](coco8.md): A smaller subset of the COCO dataset, COCO8 is more lightweight and faster to train.
- [**GlobalWheat2020**](globalwheat2020.md): A dataset containing images of wheat heads for the Global Wheat Challenge 2020.
- [**Objects365**](objects365.md): A large-scale object detection dataset with 365 object categories and 600k images, aimed at advancing object detection research.