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

Add FastSAM docs page (#3371)

Browse source
This commit is contained in:
Glenn Jocher 2023-06-25 14:08:19 +02:00 • committed by GitHub
parent 15c027a9fc
commit ca2c8774d8
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
3 changed files with 108 additions and 2 deletions
Download patch file Download diff file Expand all files Collapse all files

5
docs/models/index.md
View file

@ -17,8 +17,9 @@ In this documentation, we provide information on four major models:
5. [YOLOv7](./yolov7.md): Updated YOLO models released in 2022 by the authors of YOLOv4.
6. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
7. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
8. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
9. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
8. [Fast Segment Anything Model (FastSAM)](./fast-sam.md): FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
9. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
10. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
You can use many of these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python: