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

ultralytics 8.0.134 add MobileSAM support (#3474)

Browse source 
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Ayush Chaurasia <ayush.chaurarsia@gmail.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
This commit is contained in:
Chaoning Zhang 2023-07-13 20:25:56 +08:00 • committed by GitHub
parent c55a98ab8e
commit 201e69e4e4
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
32 changed files with 1472 additions and 841 deletions
Download patch file Download diff file Expand all files Collapse all files

3
docs/models/index.md
View file

@ -17,6 +17,7 @@ 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).
7. [Mobile Segment Anything Model (MobileSAM)](./mobile-sam.md): MobileSAM for mobile applications by Kyung Hee University.
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.
@ -44,4 +45,4 @@ model.info() # display model information
model.train(data="coco128.yaml", epochs=100) # train the model
```
For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.