Dataset Docs updates (#13015)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Burhan <62214284+Burhan-Q@users.noreply.github.com>

docs/en/datasets/index.md

@@ -30,7 +30,7 @@ Create embeddings for your dataset, search for similar images, run SQL queries,
 - Try the [GUI Demo](explorer/index.md)
 - Learn more about the [Explorer API](explorer/index.md)
 
-## [Detection Datasets](detect/index.md)
+## [Object Detection](detect/index.md)
 
 Bounding box object detection is a computer vision technique that involves detecting and localizing objects in an image by drawing a bounding box around each object.
 
@@ -50,7 +50,7 @@ Bounding box object detection is a computer vision technique that involves detec
 - [African-wildlife](detect/african-wildlife.md): A dataset featuring images of African wildlife, including buffalo, elephant, rhino, and zebras.
 - [Signature](detect/signature.md): A dataset featuring images of various documents with annotated signatures, supporting document verification and fraud detection research.
 
-## [Instance Segmentation Datasets](segment/index.md)
+## [Instance Segmentation](segment/index.md)
 
 Instance segmentation is a computer vision technique that involves identifying and localizing objects in an image at the pixel level.
 
@@ -87,7 +87,7 @@ Image classification is a computer vision task that involves categorizing an ima
 
 Oriented Bounding Boxes (OBB) is a method in computer vision for detecting angled objects in images using rotated bounding boxes, often applied to aerial and satellite imagery.
 
-- [DOTAv2](obb/dota-v2.md): A popular OBB aerial imagery dataset with 1.7 million instances and 11,268 images.
+- [DOTA-v2](obb/dota-v2.md): A popular OBB aerial imagery dataset with 1.7 million instances and 11,268 images.
 
 ## [Multi-Object Tracking](track/index.md)
 
@@ -103,11 +103,8 @@ Contributing a new dataset involves several steps to ensure that it aligns well
 ### Steps to Contribute a New Dataset
 
 1. **Collect Images**: Gather the images that belong to the dataset. These could be collected from various sources, such as public databases or your own collection.
-
 2. **Annotate Images**: Annotate these images with bounding boxes, segments, or keypoints, depending on the task.
-
 3. **Export Annotations**: Convert these annotations into the YOLO `*.txt` file format which Ultralytics supports.
-
 4. **Organize Dataset**: Arrange your dataset into the correct folder structure. You should have `train/` and `val/` top-level directories, and within each, an `images/` and `labels/` subdirectory.
 
     ```
@@ -121,11 +118,8 @@ Contributing a new dataset involves several steps to ensure that it aligns well
     ```
 
 5. **Create a `data.yaml` File**: In your dataset's root directory, create a `data.yaml` file that describes the dataset, classes, and other necessary information.
-
 6. **Optimize Images (Optional)**: If you want to reduce the size of the dataset for more efficient processing, you can optimize the images using the code below. This is not required, but recommended for smaller dataset sizes and faster download speeds.
-
 7. **Zip Dataset**: Compress the entire dataset folder into a zip file.
-
 8. **Document and PR**: Create a documentation page describing your dataset and how it fits into the existing framework. After that, submit a Pull Request (PR). Refer to [Ultralytics Contribution Guidelines](https://docs.ultralytics.com/help/contributing) for more details on how to submit a PR.
 
 ### Example Code to Optimize and Zip a Dataset

docs/en/datasets/obb/index.md

@@ -57,9 +57,8 @@ To train a model using these OBB formats:
 
 Currently, the following datasets with Oriented Bounding Boxes are supported:
 
-- [**DOTA v2**](dota-v2.md): DOTA (A Large-scale Dataset for Object Detection in Aerial Images) version 2, emphasizes detection from aerial perspectives and contains oriented bounding boxes with 1.7 million instances and 11,268 images.
-
-- [**DOTA8**](dota8.md): A small, 8-image subset of the full DOTA dataset suitable for testing workflows and Continuous Integration (CI) checks of OBB training in the `ultralytics` repository.
+- [DOTA-v2](dota-v2.md): DOTA (A Large-scale Dataset for Object Detection in Aerial Images) version 2, emphasizes detection from aerial perspectives and contains oriented bounding boxes with 1.7 million instances and 11,268 images.
+- [DOTA8](dota8.md): A small, 8-image subset of the full DOTA dataset suitable for testing workflows and Continuous Integration (CI) checks of OBB training in the `ultralytics` repository.
 
 ### Incorporating your own OBB dataset
 

docs/en/datasets/segment/index.md

@@ -91,13 +91,9 @@ The `train` and `val` fields specify the paths to the directories containing the
 ## Supported Datasets
 
 - [COCO](coco.md): A comprehensive dataset for object detection, segmentation, and captioning, featuring over 200K labeled images across a wide range of categories.
-
 - [COCO8-seg](coco8-seg.md): A compact, 8-image subset of COCO designed for quick testing of segmentation model training, ideal for CI checks and workflow validation in the `ultralytics` repository.
-
 - [Carparts-seg](carparts-seg.md): A specialized dataset focused on the segmentation of car parts, ideal for automotive applications. It includes a variety of vehicles with detailed annotations of individual car components.
-
 - [Crack-seg](crack-seg.md): A dataset tailored for the segmentation of cracks in various surfaces. Essential for infrastructure maintenance and quality control, it provides detailed imagery for training models to identify structural weaknesses.
-
 - [Package-seg](package-seg.md): A dataset dedicated to the segmentation of different types of packaging materials and shapes. It's particularly useful for logistics and warehouse automation, aiding in the development of systems for package handling and sorting.
 
 ### Adding your own dataset

ultralytics/data/build.py

@@ -128,7 +128,7 @@ def build_dataloader(dataset, batch, workers, shuffle=True, rank=-1):
     """Return an InfiniteDataLoader or DataLoader for training or validation set."""
     batch = min(batch, len(dataset))
     nd = torch.cuda.device_count()  # number of CUDA devices
-    nw = min([os.cpu_count() // max(nd, 1), workers])  # number of workers
+    nw = min(os.cpu_count() // max(nd, 1), workers)  # number of workers
     sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
     generator = torch.Generator()
     generator.manual_seed(6148914691236517205 + RANK)