Reformat Markdown code blocks (#12795)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/datasets/classify/caltech101.md

@@ -36,10 +36,10 @@ To train a YOLO model on the Caltech-101 dataset for 100 epochs, you can use the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='caltech101', epochs=100, imgsz=416)
+        results = model.train(data="caltech101", epochs=100, imgsz=416)
         ```
 
     === "CLI"

docs/en/datasets/classify/caltech256.md

@@ -36,10 +36,10 @@ To train a YOLO model on the Caltech-256 dataset for 100 epochs, you can use the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='caltech256', epochs=100, imgsz=416)
+        results = model.train(data="caltech256", epochs=100, imgsz=416)
         ```
 
     === "CLI"

docs/en/datasets/classify/cifar10.md

@@ -39,10 +39,10 @@ To train a YOLO model on the CIFAR-10 dataset for 100 epochs with an image size
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='cifar10', epochs=100, imgsz=32)
+        results = model.train(data="cifar10", epochs=100, imgsz=32)
         ```
 
     === "CLI"

docs/en/datasets/classify/cifar100.md

@@ -39,10 +39,10 @@ To train a YOLO model on the CIFAR-100 dataset for 100 epochs with an image size
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='cifar100', epochs=100, imgsz=32)
+        results = model.train(data="cifar100", epochs=100, imgsz=32)
         ```
 
     === "CLI"

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

@@ -53,10 +53,10 @@ To train a CNN model on the Fashion-MNIST dataset for 100 epochs with an image s
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='fashion-mnist', epochs=100, imgsz=28)
+        results = model.train(data="fashion-mnist", epochs=100, imgsz=28)
         ```
 
     === "CLI"

docs/en/datasets/classify/imagenet.md

@@ -49,10 +49,10 @@ To train a deep learning model on the ImageNet dataset for 100 epochs with an im
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='imagenet', epochs=100, imgsz=224)
+        results = model.train(data="imagenet", epochs=100, imgsz=224)
         ```
 
     === "CLI"

docs/en/datasets/classify/imagenet10.md

@@ -35,10 +35,10 @@ To test a deep learning model on the ImageNet10 dataset with an image size of 22
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='imagenet10', epochs=5, imgsz=224)
+        results = model.train(data="imagenet10", epochs=5, imgsz=224)
         ```
 
     === "CLI"

docs/en/datasets/classify/imagenette.md

@@ -37,10 +37,10 @@ To train a model on the ImageNette dataset for 100 epochs with a standard image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='imagenette', epochs=100, imgsz=224)
+        results = model.train(data="imagenette", epochs=100, imgsz=224)
         ```
 
     === "CLI"
@@ -72,10 +72,10 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model with ImageNette160
-        results = model.train(data='imagenette160', epochs=100, imgsz=160)
+        results = model.train(data="imagenette160", epochs=100, imgsz=160)
         ```
 
     === "CLI"
@@ -93,10 +93,10 @@ To use these datasets, simply replace 'imagenette' with 'imagenette160' or 'imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model with ImageNette320
-        results = model.train(data='imagenette320', epochs=100, imgsz=320)
+        results = model.train(data="imagenette320", epochs=100, imgsz=320)
         ```
 
     === "CLI"

docs/en/datasets/classify/imagewoof.md

@@ -34,10 +34,10 @@ To train a CNN model on the ImageWoof dataset for 100 epochs with an image size
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='imagewoof', epochs=100, imgsz=224)
+        results = model.train(data="imagewoof", epochs=100, imgsz=224)
         ```
 
     === "CLI"
@@ -63,13 +63,13 @@ To use these variants in your training, simply replace 'imagewoof' in the datase
 from ultralytics import YOLO
 
 # Load a model
-model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
 # For medium-sized dataset
-model.train(data='imagewoof320', epochs=100, imgsz=224)
+model.train(data="imagewoof320", epochs=100, imgsz=224)
 
 # For small-sized dataset
-model.train(data='imagewoof160', epochs=100, imgsz=224)
+model.train(data="imagewoof160", epochs=100, imgsz=224)
 ```
 
 It's important to note that using smaller images will likely yield lower performance in terms of classification accuracy. However, it's an excellent way to iterate quickly in the early stages of model development and prototyping.

docs/en/datasets/classify/index.md

@@ -86,10 +86,10 @@ This structured approach ensures that the model can effectively learn from well-
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='path/to/dataset', epochs=100, imgsz=640)
+        results = model.train(data="path/to/dataset", epochs=100, imgsz=640)
         ```
     === "CLI"
 

docs/en/datasets/classify/mnist.md

@@ -42,10 +42,10 @@ To train a CNN model on the MNIST dataset for 100 epochs with an image size of 3
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-cls.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-cls.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='mnist', epochs=100, imgsz=32)
+        results = model.train(data="mnist", epochs=100, imgsz=32)
         ```
 
     === "CLI"

docs/en/datasets/detect/african-wildlife.md

@@ -42,10 +42,10 @@ To train a YOLOv8n model on the African wildlife dataset for 100 epochs with an
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='african-wildlife.yaml', epochs=100, imgsz=640)
+        results = model.train(data="african-wildlife.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
@@ -63,7 +63,7 @@ To train a YOLOv8n model on the African wildlife dataset for 100 epochs with an
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('path/to/best.pt')  # load a brain-tumor fine-tuned model
+        model = YOLO("path/to/best.pt")  # load a brain-tumor fine-tuned model
 
         # Inference using the model
         results = model.predict("https://ultralytics.com/assets/african-wildlife-sample.jpg")

docs/en/datasets/detect/argoverse.md

@@ -53,10 +53,10 @@ To train a YOLOv8n model on the Argoverse dataset for 100 epochs with an image s
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='Argoverse.yaml', epochs=100, imgsz=640)
+        results = model.train(data="Argoverse.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/brain-tumor.md

@@ -52,10 +52,10 @@ To train a YOLOv8n model on the brain tumor dataset for 100 epochs with an image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='brain-tumor.yaml', epochs=100, imgsz=640)
+        results = model.train(data="brain-tumor.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
@@ -73,7 +73,7 @@ To train a YOLOv8n model on the brain tumor dataset for 100 epochs with an image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('path/to/best.pt')  # load a brain-tumor fine-tuned model
+        model = YOLO("path/to/best.pt")  # load a brain-tumor fine-tuned model
 
         # Inference using the model
         results = model.predict("https://ultralytics.com/assets/brain-tumor-sample.jpg")

docs/en/datasets/detect/coco.md

@@ -70,10 +70,10 @@ To train a YOLOv8n model on the COCO dataset for 100 epochs with an image size o
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/coco8.md

@@ -45,10 +45,10 @@ To train a YOLOv8n model on the COCO8 dataset for 100 epochs with an image size
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/globalwheat2020.md

@@ -48,10 +48,10 @@ To train a YOLOv8n model on the Global Wheat Head Dataset for 100 epochs with an
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='GlobalWheat2020.yaml', epochs=100, imgsz=640)
+        results = model.train(data="GlobalWheat2020.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/index.md

@@ -56,10 +56,10 @@ Here's how you can use these formats to train your model:
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8.yaml", epochs=100, imgsz=640)
         ```
     === "CLI"
 
@@ -103,7 +103,7 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
         ```python
         from ultralytics.data.converter import convert_coco
 
-        convert_coco(labels_dir='path/to/coco/annotations/')
+        convert_coco(labels_dir="path/to/coco/annotations/")
         ```
 
 This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.

docs/en/datasets/detect/lvis.md

@@ -66,10 +66,10 @@ To train a YOLOv8n model on the LVIS dataset for 100 epochs with an image size o
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='lvis.yaml', epochs=100, imgsz=640)
+        results = model.train(data="lvis.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/objects365.md

@@ -48,10 +48,10 @@ To train a YOLOv8n model on the Objects365 dataset for 100 epochs with an image
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='Objects365.yaml', epochs=100, imgsz=640)
+        results = model.train(data="Objects365.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/open-images-v7.md

@@ -88,10 +88,10 @@ To train a YOLOv8n model on the Open Images V7 dataset for 100 epochs with an im
         from ultralytics import YOLO
 
         # Load a COCO-pretrained YOLOv8n model
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
 
         # Train the model on the Open Images V7 dataset
-        results = model.train(data='open-images-v7.yaml', epochs=100, imgsz=640)
+        results = model.train(data="open-images-v7.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/roboflow-100.md

@@ -46,39 +46,40 @@ Dataset benchmarking evaluates machine learning model performance on specific da
     === "Python"
 
         ```python
-        from pathlib import Path
-        import shutil
         import os
+        import shutil
+        from pathlib import Path
+
         from ultralytics.utils.benchmarks import RF100Benchmark
-        
+
         # Initialize RF100Benchmark and set API key
         benchmark = RF100Benchmark()
         benchmark.set_key(api_key="YOUR_ROBOFLOW_API_KEY")
-        
+
         # Parse dataset and define file paths
         names, cfg_yamls = benchmark.parse_dataset()
         val_log_file = Path("ultralytics-benchmarks") / "validation.txt"
         eval_log_file = Path("ultralytics-benchmarks") / "evaluation.txt"
-        
+
         # Run benchmarks on each dataset in RF100
         for ind, path in enumerate(cfg_yamls):
             path = Path(path)
             if path.exists():
                 # Fix YAML file and run training
                 benchmark.fix_yaml(str(path))
-                os.system(f'yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16')
-                
+                os.system(f"yolo detect train data={path} model=yolov8s.pt epochs=1 batch=16")
+
                 # Run validation and evaluate
-                os.system(f'yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1')
+                os.system(f"yolo detect val data={path} model=runs/detect/train/weights/best.pt > {val_log_file} 2>&1")
                 benchmark.evaluate(str(path), str(val_log_file), str(eval_log_file), ind)
-                
+
                 # Remove the 'runs' directory
                 runs_dir = Path.cwd() / "runs"
                 shutil.rmtree(runs_dir)
             else:
                 print("YAML file path does not exist")
                 continue
-        
+
         print("RF100 Benchmarking completed!")
         ```
 

docs/en/datasets/detect/sku-110k.md

@@ -50,10 +50,10 @@ To train a YOLOv8n model on the SKU-110K dataset for 100 epochs with an image si
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='SKU-110K.yaml', epochs=100, imgsz=640)
+        results = model.train(data="SKU-110K.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/visdrone.md

@@ -46,10 +46,10 @@ To train a YOLOv8n model on the VisDrone dataset for 100 epochs with an image si
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='VisDrone.yaml', epochs=100, imgsz=640)
+        results = model.train(data="VisDrone.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/voc.md

@@ -49,10 +49,10 @@ To train a YOLOv8n model on the VOC dataset for 100 epochs with an image size of
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='VOC.yaml', epochs=100, imgsz=640)
+        results = model.train(data="VOC.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/detect/xview.md

@@ -52,10 +52,10 @@ To train a model on the xView dataset for 100 epochs with an image size of 640,
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='xView.yaml', epochs=100, imgsz=640)
+        results = model.train(data="xView.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/explorer/api.md

@@ -36,13 +36,13 @@ pip install ultralytics[explorer]
 from ultralytics import Explorer
 
 # Create an Explorer object
-explorer = Explorer(data='coco128.yaml', model='yolov8n.pt')
+explorer = Explorer(data="coco128.yaml", model="yolov8n.pt")
 
 # Create embeddings for your dataset
 explorer.create_embeddings_table()
 
 # Search for similar images to a given image/images
-dataframe = explorer.get_similar(img='path/to/image.jpg')
+dataframe = explorer.get_similar(img="path/to/image.jpg")
 
 # Or search for similar images to a given index/indices
 dataframe = explorer.get_similar(idx=0)
@@ -75,18 +75,17 @@ You get a pandas dataframe with the `limit` number of most similar data points t
         from ultralytics import Explorer
 
         # create an Explorer object
-        exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
+        exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
         exp.create_embeddings_table()
 
-        similar = exp.get_similar(img='https://ultralytics.com/images/bus.jpg', limit=10)
+        similar = exp.get_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
         print(similar.head())
 
         # Search using multiple indices
         similar = exp.get_similar(
-                                img=['https://ultralytics.com/images/bus.jpg',
-                                     'https://ultralytics.com/images/bus.jpg'],
-                                limit=10
-                                )
+            img=["https://ultralytics.com/images/bus.jpg", "https://ultralytics.com/images/bus.jpg"],
+            limit=10,
+        )
         print(similar.head())
         ```
 
@@ -96,14 +95,14 @@ You get a pandas dataframe with the `limit` number of most similar data points t
         from ultralytics import Explorer
 
         # create an Explorer object
-        exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
+        exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
         exp.create_embeddings_table()
 
         similar = exp.get_similar(idx=1, limit=10)
         print(similar.head())
 
         # Search using multiple indices
-        similar = exp.get_similar(idx=[1,10], limit=10)
+        similar = exp.get_similar(idx=[1, 10], limit=10)
         print(similar.head())
         ```
 
@@ -119,10 +118,10 @@ You can also plot the similar images using the `plot_similar` method. This metho
         from ultralytics import Explorer
 
         # create an Explorer object
-        exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
+        exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
         exp.create_embeddings_table()
 
-        plt = exp.plot_similar(img='https://ultralytics.com/images/bus.jpg', limit=10)
+        plt = exp.plot_similar(img="https://ultralytics.com/images/bus.jpg", limit=10)
         plt.show()
         ```
 
@@ -132,7 +131,7 @@ You can also plot the similar images using the `plot_similar` method. This metho
         from ultralytics import Explorer
 
         # create an Explorer object
-        exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
+        exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
         exp.create_embeddings_table()
 
         plt = exp.plot_similar(idx=1, limit=10)
@@ -150,9 +149,8 @@ Note: This works using LLMs under the hood so the results are probabilistic and
     from ultralytics import Explorer
     from ultralytics.data.explorer import plot_query_result
 
-
     # create an Explorer object
-    exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
+    exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
     exp.create_embeddings_table()
 
     df = exp.ask_ai("show me 100 images with exactly one person and 2 dogs. There can be other objects too")
@@ -173,7 +171,7 @@ You can run SQL queries on your dataset using the `sql_query` method. This metho
     from ultralytics import Explorer
 
     # create an Explorer object
-    exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
+    exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
     exp.create_embeddings_table()
 
     df = exp.sql_query("WHERE labels LIKE '%person%' AND labels LIKE '%dog%'")
@@ -190,7 +188,7 @@ You can also plot the results of a SQL query using the `plot_sql_query` method.
     from ultralytics import Explorer
 
     # create an Explorer object
-    exp = Explorer(data='coco128.yaml', model='yolov8n.pt')
+    exp = Explorer(data="coco128.yaml", model="yolov8n.pt")
     exp.create_embeddings_table()
 
     # plot the SQL Query
@@ -293,7 +291,7 @@ You can use similarity index to build custom conditions to filter out the datase
 import numpy as np
 
 sim_count = np.array(sim_idx["count"])
-sim_idx['im_file'][sim_count > 30]
+sim_idx["im_file"][sim_count > 30]
 ```
 
 ### Visualize Embedding Space
@@ -301,10 +299,10 @@ sim_idx['im_file'][sim_count > 30]
 You can also visualize the embedding space using the plotting tool of your choice. For example here is a simple example using matplotlib:
 
 ```python
-import numpy as np
-from sklearn.decomposition import PCA
 import matplotlib.pyplot as plt
+import numpy as np
 from mpl_toolkits.mplot3d import Axes3D
+from sklearn.decomposition import PCA
 
 # Reduce dimensions using PCA to 3 components for visualization in 3D
 pca = PCA(n_components=3)
@@ -312,14 +310,14 @@ reduced_data = pca.fit_transform(embeddings)
 
 # Create a 3D scatter plot using Matplotlib Axes3D
 fig = plt.figure(figsize=(8, 6))
-ax = fig.add_subplot(111, projection='3d')
+ax = fig.add_subplot(111, projection="3d")
 
 # Scatter plot
 ax.scatter(reduced_data[:, 0], reduced_data[:, 1], reduced_data[:, 2], alpha=0.5)
-ax.set_title('3D Scatter Plot of Reduced 256-Dimensional Data (PCA)')
-ax.set_xlabel('Component 1')
-ax.set_ylabel('Component 2')
-ax.set_zlabel('Component 3')
+ax.set_title("3D Scatter Plot of Reduced 256-Dimensional Data (PCA)")
+ax.set_xlabel("Component 1")
+ax.set_ylabel("Component 2")
+ax.set_zlabel("Component 3")
 
 plt.show()
 ```

docs/en/datasets/index.md

@@ -135,14 +135,15 @@ Contributing a new dataset involves several steps to ensure that it aligns well
 
     ```python
     from pathlib import Path
+
     from ultralytics.data.utils import compress_one_image
     from ultralytics.utils.downloads import zip_directory
 
     # Define dataset directory
-    path = Path('path/to/dataset')
+    path = Path("path/to/dataset")
 
     # Optimize images in dataset (optional)
-    for f in path.rglob('*.jpg'):
+    for f in path.rglob("*.jpg"):
         compress_one_image(f)
 
     # Zip dataset into 'path/to/dataset.zip'

docs/en/datasets/obb/dota-v2.md

@@ -75,21 +75,21 @@ To train DOTA dataset, we split original DOTA images with high-resolution into i
     === "Python"
 
         ```python
-        from ultralytics.data.split_dota import split_trainval, split_test
+        from ultralytics.data.split_dota import split_test, split_trainval
 
         # split train and val set, with labels.
         split_trainval(
-            data_root='path/to/DOTAv1.0/',
-            save_dir='path/to/DOTAv1.0-split/',
-            rates=[0.5, 1.0, 1.5],    # multiscale
-            gap=500
+            data_root="path/to/DOTAv1.0/",
+            save_dir="path/to/DOTAv1.0-split/",
+            rates=[0.5, 1.0, 1.5],  # multiscale
+            gap=500,
         )
         # split test set, without labels.
         split_test(
-            data_root='path/to/DOTAv1.0/',
-            save_dir='path/to/DOTAv1.0-split/',
-            rates=[0.5, 1.0, 1.5],    # multiscale
-            gap=500
+            data_root="path/to/DOTAv1.0/",
+            save_dir="path/to/DOTAv1.0-split/",
+            rates=[0.5, 1.0, 1.5],  # multiscale
+            gap=500,
         )
         ```
 
@@ -109,10 +109,10 @@ To train a model on the DOTA v1 dataset, you can utilize the following code snip
         from ultralytics import YOLO
 
         # Create a new YOLOv8n-OBB model from scratch
-        model = YOLO('yolov8n-obb.yaml')
+        model = YOLO("yolov8n-obb.yaml")
 
         # Train the model on the DOTAv2 dataset
-        results = model.train(data='DOTAv1.yaml', epochs=100, imgsz=640)
+        results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/obb/dota8.md

@@ -34,10 +34,10 @@ To train a YOLOv8n-obb model on the DOTA8 dataset for 100 epochs with an image s
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-obb.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-obb.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='dota8.yaml', epochs=100, imgsz=640)
+        results = model.train(data="dota8.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/obb/index.md

@@ -40,10 +40,10 @@ To train a model using these OBB formats:
         from ultralytics import YOLO
 
         # Create a new YOLOv8n-OBB model from scratch
-        model = YOLO('yolov8n-obb.yaml')
+        model = YOLO("yolov8n-obb.yaml")
 
         # Train the model on the DOTAv2 dataset
-        results = model.train(data='DOTAv1.yaml', epochs=100, imgsz=640)
+        results = model.train(data="DOTAv1.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"
@@ -78,7 +78,7 @@ Transitioning labels from the DOTA dataset format to the YOLO OBB format can be
         ```python
         from ultralytics.data.converter import convert_dota_to_yolo_obb
 
-        convert_dota_to_yolo_obb('path/to/DOTA')
+        convert_dota_to_yolo_obb("path/to/DOTA")
         ```
 
 This conversion mechanism is instrumental for datasets in the DOTA format, ensuring alignment with the Ultralytics YOLO OBB format.

docs/en/datasets/pose/coco.md

@@ -61,10 +61,10 @@ To train a YOLOv8n-pose model on the COCO-Pose dataset for 100 epochs with an im
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco-pose.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco-pose.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/pose/coco8-pose.md

@@ -34,10 +34,10 @@ To train a YOLOv8n-pose model on the COCO8-Pose dataset for 100 epochs with an i
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/pose/index.md

@@ -72,10 +72,10 @@ The `train` and `val` fields specify the paths to the directories containing the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8-pose.yaml", epochs=100, imgsz=640)
         ```
     === "CLI"
 
@@ -132,7 +132,7 @@ Ultralytics provides a convenient conversion tool to convert labels from the pop
         ```python
         from ultralytics.data.converter import convert_coco
 
-        convert_coco(labels_dir='path/to/coco/annotations/', use_keypoints=True)
+        convert_coco(labels_dir="path/to/coco/annotations/", use_keypoints=True)
         ```
 
 This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.

docs/en/datasets/pose/tiger-pose.md

@@ -47,10 +47,10 @@ To train a YOLOv8n-pose model on the Tiger-Pose dataset for 100 epochs with an i
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-pose.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-pose.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='tiger-pose.yaml', epochs=100, imgsz=640)
+        results = model.train(data="tiger-pose.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/carparts-seg.md

@@ -55,10 +55,10 @@ To train Ultralytics YOLOv8n model on the Carparts Segmentation dataset for 100
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='carparts-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="carparts-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/coco.md

@@ -59,10 +59,10 @@ To train a YOLOv8n-seg model on the COCO-Seg dataset for 100 epochs with an imag
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/coco8-seg.md

@@ -34,10 +34,10 @@ To train a YOLOv8n-seg model on the COCO8-Seg dataset for 100 epochs with an ima
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/crack-seg.md

@@ -44,10 +44,10 @@ To train Ultralytics YOLOv8n model on the Crack Segmentation dataset for 100 epo
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='crack-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="crack-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/segment/index.md

@@ -74,10 +74,10 @@ The `train` and `val` fields specify the paths to the directories containing the
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='coco8-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="coco8-seg.yaml", epochs=100, imgsz=640)
         ```
     === "CLI"
 
@@ -117,7 +117,7 @@ You can easily convert labels from the popular COCO dataset format to the YOLO f
         ```python
         from ultralytics.data.converter import convert_coco
 
-        convert_coco(labels_dir='path/to/coco/annotations/', use_segments=True)
+        convert_coco(labels_dir="path/to/coco/annotations/", use_segments=True)
         ```
 
 This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format.
@@ -139,7 +139,7 @@ To auto-annotate your dataset using the Ultralytics framework, you can use the `
         ```python
         from ultralytics.data.annotator import auto_annotate
 
-        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
+        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model="sam_b.pt")
         ```
 
 Certainly, here is the table updated with code snippets:

docs/en/datasets/segment/package-seg.md

@@ -44,10 +44,10 @@ To train Ultralytics YOLOv8n model on the Package Segmentation dataset for 100 e
         from ultralytics import YOLO
 
         # Load a model
-        model = YOLO('yolov8n-seg.pt')  # load a pretrained model (recommended for training)
+        model = YOLO("yolov8n-seg.pt")  # load a pretrained model (recommended for training)
 
         # Train the model
-        results = model.train(data='package-seg.yaml', epochs=100, imgsz=640)
+        results = model.train(data="package-seg.yaml", epochs=100, imgsz=640)
         ```
 
     === "CLI"

docs/en/datasets/track/index.md

@@ -19,7 +19,7 @@ Multi-Object Detector doesn't need standalone training and directly supports pre
         ```python
         from ultralytics import YOLO
 
-        model = YOLO('yolov8n.pt')
+        model = YOLO("yolov8n.pt")
         results = model.track(source="https://youtu.be/LNwODJXcvt4", conf=0.3, iou=0.5, show=True)
         ```
     === "CLI"