Update `pyproject.toml` and Docs (#7274)

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>

.pre-commit-config.yaml

@@ -32,7 +32,7 @@ repos:
         name: Upgrade code
 
   - repo: https://github.com/PyCQA/isort
-    rev: 5.12.0
+    rev: 5.13.2
     hooks:
       - id: isort
         name: Sort imports

docs/en/integrations/index.md

@@ -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.
 

docs/en/reference/data/augment.md

@@ -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>

docs/mkdocs.yml

@@ -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

pyproject.toml

@@ -1,5 +1,5 @@
 # Ultralytics YOLO 🚀, AGPL-3.0 license
-#
+
 # Overview:
 # This pyproject.toml file manages the build, packaging, and distribution of the Ultralytics library.
 # It defines essential project metadata, dependencies, and settings used to develop and deploy the library.
@@ -159,6 +159,9 @@ space_between_ending_comma_and_closing_bracket = true
 split_before_closing_bracket = false
 split_before_first_argument = false
 
+[tool.ruff]
+line-length = 120
+
 [tool.docformatter]
 wrap-summaries = 120
 wrap-descriptions = 120
@@ -167,5 +170,5 @@ pre-summary-newline = true
 close-quotes-on-newline = true
 
 [tool.codespell]
-ignore-words-list = "crate,nd,strack,dota,ane,segway,fo,gool,winn"
-skip = '*.csv,*venv*,docs/de,docs/fr,docs/pt,docs/es,docs/mkdocs_de.yml'
+ignore-words-list = "crate,nd,strack,dota,ane,segway,fo,gool,winn,commend"
+skip = '*.csv,*venv*,docs/??/,docs/mkdocs_??.yml'

ultralytics/data/augment.py

@@ -1005,7 +1005,7 @@ def classify_transforms(
         crop_fraction (float): fraction of image to crop. default is 1.0.
 
     Returns:
-        T.Compose: torchvision transforms
+        (T.Compose): torchvision transforms
     """
 
     if isinstance(size, (tuple, list)):
@@ -1064,13 +1064,12 @@ def classify_augmentations(
         hsv_h (float): image HSV-Hue augmentation (fraction)
         hsv_s (float): image HSV-Saturation augmentation (fraction)
         hsv_v (float): image HSV-Value augmentation (fraction)
-        contrast (float): image contrast augmentation (fraction)
         force_color_jitter (bool): force to apply color jitter even if auto augment is enabled
         erasing (float): probability of random erasing
         interpolation (T.InterpolationMode): interpolation mode. default is T.InterpolationMode.BILINEAR.
 
     Returns:
-        T.Compose: torchvision transforms
+        (T.Compose): torchvision transforms
     """
     # Transforms to apply if albumentations not installed
     if not isinstance(size, int):