Fix gitignore to format Docs datasets (#16071)

Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/classify/caltech101.md

@@ -110,11 +110,13 @@ To train an Ultralytics YOLO model on the Caltech-101 dataset, you can use the p
         # Start training from a pretrained *.pt model
         yolo classify train data=caltech101 model=yolov8n-cls.pt epochs=100 imgsz=416
         ```
+
 For more detailed arguments and options, refer to the model [Training](../../modes/train.md) page.
 
 ### What are the key features of the Caltech-101 dataset?
 
 The Caltech-101 dataset includes:
+
 - Around 9,000 color images across 101 categories.
 - Categories covering a diverse range of objects, including animals, vehicles, and household items.
 - Variable number of images per category, typically between 40 and 800.
@@ -142,6 +144,7 @@ Citing the Caltech-101 dataset in your research acknowledges the creators' contr
           publisher={Elsevier}
         }
         ```
+
 Citing helps in maintaining the integrity of academic work and assists peers in locating the original resource.
 
 ### Can I use Ultralytics HUB for training models on the Caltech-101 dataset?

docs/en/datasets/classify/cifar100.md

@@ -92,7 +92,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
 !!! Example "Train Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
 
@@ -104,7 +104,7 @@ You can train a YOLO model on the CIFAR-100 dataset using either Python or CLI c
         ```
 
     === "CLI"
-        
+
         ```bash
         # Start training from a pretrained *.pt model
         yolo classify train data=cifar100 model=yolov8n-cls.pt epochs=100 imgsz=32

docs/en/datasets/classify/fashion-mnist.md

@@ -102,7 +102,7 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
 !!! Example "Train Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
 
@@ -112,10 +112,10 @@ To train an Ultralytics YOLO model on the Fashion-MNIST dataset, you can use bot
         # Train the model on Fashion-MNIST
         results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
         ```
-    
+
 
     === "CLI"
-    
+
         ```bash
         yolo classify train data=fashion-mnist model=yolov8n-cls.pt epochs=100 imgsz=28
         ```

docs/en/datasets/classify/imagenet.md

@@ -11,7 +11,7 @@ keywords: ImageNet, deep learning, visual recognition, computer vision, pretrain
 ## ImageNet Pretrained Models
 
 | Model                                                                                        | size<br><sup>(pixels) | acc<br><sup>top1 | acc<br><sup>top5 | Speed<br><sup>CPU ONNX<br>(ms) | Speed<br><sup>A100 TensorRT<br>(ms) | params<br><sup>(M) | FLOPs<br><sup>(B) at 640 |
-|----------------------------------------------------------------------------------------------|-----------------------|------------------|------------------|--------------------------------|-------------------------------------|--------------------|--------------------------|
+| -------------------------------------------------------------------------------------------- | --------------------- | ---------------- | ---------------- | ------------------------------ | ----------------------------------- | ------------------ | ------------------------ |
 | [YOLOv8n-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8n-cls.pt) | 224                   | 69.0             | 88.3             | 12.9                           | 0.31                                | 2.7                | 4.3                      |
 | [YOLOv8s-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8s-cls.pt) | 224                   | 73.8             | 91.7             | 23.4                           | 0.35                                | 6.4                | 13.5                     |
 | [YOLOv8m-cls](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8m-cls.pt) | 224                   | 76.8             | 93.5             | 85.4                           | 0.62                                | 17.0               | 42.7                     |
@@ -105,7 +105,7 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
 !!! Example "Train Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
 
@@ -117,7 +117,7 @@ To use a pretrained Ultralytics YOLO model for image classification on the Image
         ```
 
     === "CLI"
-    
+
         ```bash
         # Start training from a pretrained *.pt model
         yolo classify train data=imagenet model=yolov8n-cls.pt epochs=100 imgsz=224

docs/en/datasets/classify/imagenette.md

@@ -152,12 +152,12 @@ The ImageNette dataset is advantageous for several reasons:
 - **Quick and Simple**: It contains only 10 classes, making it less complex and time-consuming compared to larger datasets.
 - **Educational Use**: Ideal for learning and teaching the basics of image classification since it requires less computational power and time.
 - **Versatility**: Widely used to train and benchmark various machine learning models, especially in image classification.
-  
+
 For more details on model training and dataset management, explore the [Dataset Structure](#dataset-structure) section.
 
 ### Can the ImageNette dataset be used with different image sizes?
 
-Yes, the ImageNette dataset is also available in two resized versions: ImageNette160 and ImageNette320. These versions help in faster prototyping and are especially useful when computational resources are limited. 
+Yes, the ImageNette dataset is also available in two resized versions: ImageNette160 and ImageNette320. These versions help in faster prototyping and are especially useful when computational resources are limited.
 
 !!! Example "Train Example with ImageNette160"
 
@@ -174,7 +174,7 @@ Yes, the ImageNette dataset is also available in two resized versions: ImageNett
         ```
 
     === "CLI"
-    
+
         ```bash
         # Start training from a pretrained *.pt model with ImageNette160
         yolo detect train data=imagenette160 model=yolov8n-cls.pt epochs=100 imgsz=160

docs/en/datasets/classify/imagewoof.md

@@ -112,17 +112,17 @@ To train a Convolutional Neural Network (CNN) model on the ImageWoof dataset usi
 !!! Example "Train Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
 
         model = YOLO("yolov8n-cls.pt")  # Load a pretrained model
         results = model.train(data="imagewoof", epochs=100, imgsz=224)
         ```
-    
+
 
     === "CLI"
-    
+
         ```bash
         yolo classify train data=imagewoof model=yolov8n-cls.pt epochs=100 imgsz=224
         ```

docs/en/datasets/classify/index.md

@@ -197,7 +197,7 @@ Training a model using Ultralytics YOLO can be done easily in both Python and CL
 !!! Example
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
 
@@ -207,10 +207,10 @@ Training a model using Ultralytics YOLO can be done easily in both Python and CL
         # Train the model
         results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
         ```
-    
+
 
     === "CLI"
-    
+
         ```bash
         # Start training from a pretrained *.pt model
         yolo detect train data=path/to/data model=yolov8n-cls.pt epochs=100 imgsz=640

docs/en/datasets/classify/mnist.md

@@ -98,7 +98,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
 !!! Example "Train Example"
 
     === "Python"
-    
+
         ```python
         from ultralytics import YOLO
 
@@ -110,7 +110,7 @@ To train a model on the MNIST dataset using Ultralytics YOLO, you can follow the
         ```
 
     === "CLI"
-    
+
         ```bash
         # Start training from a pretrained *.pt model
         yolo classify train data=mnist model=yolov8n-cls.pt epochs=100 imgsz=28