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

ultralytics 8.0.212 add Windows UTF-8 support (#6407)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Abirami Vina <abirami.vina@gmail.com>
This commit is contained in:
Glenn Jocher 2023-11-18 04:40:34 +01:00 • committed by GitHub
parent 14c05f0dd1
commit b6baae584c
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
19 changed files with 103 additions and 113 deletions
Download patch file Download diff file Expand all files Collapse all files

10
docs/en/models/rtdetr.md
View file

@ -71,11 +71,11 @@ You can use RT-DETR for object detection tasks using the `ultralytics` pip packa
### Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
## Citations and Acknowledgements

10
docs/en/models/sam.md
View file

@ -131,11 +131,11 @@ The Segment Anything Model can be employed for a multitude of downstream tasks t
## Operating Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :x: |
| Training | :x: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ❌ |
| Training | ❌ |
## SAM comparison vs YOLOv8

10
docs/en/models/yolo-nas.md
View file

@ -94,11 +94,11 @@ The YOLO-NAS models are primarily designed for object detection tasks. You can d
The YOLO-NAS models support both inference and validation modes, allowing you to predict and validate results with ease. Training mode, however, is currently not supported.
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :x: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ❌ |
Harness the power of the YOLO-NAS models to drive your object detection tasks to new heights of performance and speed.

10
docs/en/models/yolov5.md
View file

@ -28,11 +28,11 @@ YOLOv5u represents an advancement in object detection methodologies. Originating
## Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
!!! Performance

10
docs/en/models/yolov6.md
View file

@ -85,11 +85,11 @@ You can use YOLOv6 for object detection tasks using the Ultralytics pip package.
## Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
## Citations and Acknowledgements

10
docs/en/models/yolov8.md
View file

@ -30,11 +30,11 @@ YOLOv8 is the latest iteration in the YOLO series of real-time object detectors,
## Supported Modes
| Mode | Supported |
|------------|--------------------|
| Inference | :heavy_check_mark: |
| Validation | :heavy_check_mark: |
| Training | :heavy_check_mark: |
| Mode | Supported |
|------------|-----------|
| Inference | ✅ |
| Validation | ✅ |
| Training | ✅ |
!!! Performance