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

Update pyproject.toml and Docs (#7274)

Browse source 
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Yaofu <voipman@sina.cn>
Co-authored-by: Umit Kacar, PhD <kacarumit.phd@gmail.com>
This commit is contained in:
Glenn Jocher 2024-01-04 01:40:13 +01:00 • committed by GitHub
parent e687c09423
commit f702b34a50
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
6 changed files with 18 additions and 15 deletions
Download patch file Download diff file Expand all files Collapse all files

6
docs/en/integrations/index.md
View file

@ -30,13 +30,15 @@ Welcome to the Ultralytics Integrations page! This page provides an overview of
- [Ray Tune](ray-tune.md): Optimize the hyperparameters of your Ultralytics models at any scale.
- [TensorBoard](https://tensorboard.dev/): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.
- [TensorBoard](tensorboard.md): Visualize your Ultralytics ML workflows, monitor model metrics, and foster team collaboration.
- [Weights & Biases (W&B)](weights-biases.md): Monitor experiments, visualize metrics, and foster reproducibility and collaboration on Ultralytics projects.
- [Amazon SageMaker](amazon-sagemaker.md): Leverage Amazon SageMaker to efficiently build, train, and deploy Ultralytics models, providing an all-in-one platform for the ML lifecycle.
## Deployment Integrations
- [Neural Magic](https://neuralmagic.com/): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
- [Neural Magic](neural-magic.md): Leverage Quantization Aware Training (QAT) and pruning techniques to optimize Ultralytics models for superior performance and leaner size.
- [OpenVino](openvino.md): OpenVINO is Intel's toolkit for optimizing and deploying computer vision models efficiently across various Intel hardware platforms.

6
docs/en/reference/data/augment.md
View file

@ -79,10 +79,6 @@ keywords: Ultralytics, Data Augmentation, BaseTransform, MixUp, RandomHSV, Lette
<br><br>
## ::: ultralytics.data.augment.hsv2colorjitter
<br><br>
## ::: ultralytics.data.augment.classify_albumentations
## ::: ultralytics.data.augment.classify_augmentations
<br><br>

5
docs/mkdocs.yml
View file

@ -289,6 +289,9 @@ nav:
- ClearML: integrations/clearml.md
- DVC: integrations/dvc.md
- Weights & Biases: integrations/weights-biases.md
- Neural Magic: integrations/neural-magic.md
- TensorBoard: integrations/tensorboard.md
- Amazon SageMaker: integrations/amazon-sagemaker.md
- Usage:
- CLI: usage/cli.md
- Python: usage/python.md
@ -416,8 +419,8 @@ nav:
- tasks: reference/nn/tasks.md
- solutions:
- ai_gym: reference/solutions/ai_gym.md
- object_counter: reference/solutions/object_counter.md
- heatmap: reference/solutions/heatmap.md
- object_counter: reference/solutions/object_counter.md
- trackers:
- basetrack: reference/trackers/basetrack.md
- bot_sort: reference/trackers/bot_sort.md