Remove unused code (#4327)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/models/fast-sam.md

@@ -40,42 +40,45 @@ The FastSAM models are easy to integrate into your Python applications. Ultralyt
 
 To perform object detection on an image, use the `predict` method as shown below:
 
-```python
-from ultralytics import FastSAM
-from ultralytics.models.fastsam import FastSAMPrompt
+!!! example ""
 
-# Define image path and inference device
-IMAGE_PATH = 'ultralytics/assets/bus.jpg'
-DEVICE = 'cpu'
+    === "Python"
+        ```python
+        from ultralytics import FastSAM
+        from ultralytics.models.fastsam import FastSAMPrompt
 
-# Create a FastSAM model
-model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
+        # Define image path and inference device
+        IMAGE_PATH = 'ultralytics/assets/bus.jpg'
+        DEVICE = 'cpu'
 
-# Run inference on an image
-everything_results = model(IMAGE_PATH,
-                           device=DEVICE,
-                           retina_masks=True,
-                           imgsz=1024,
-                           conf=0.4,
-                           iou=0.9)
+        # Create a FastSAM model
+        model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
 
-prompt_process = FastSAMPrompt(IMAGE_PATH, everything_results, device=DEVICE)
+        # Run inference on an image
+        everything_results = model(IMAGE_PATH,
+                                 device=DEVICE,
+                                 retina_masks=True,
+                                 imgsz=1024,
+                                 conf=0.4,
+                                 iou=0.9)
+      
+        prompt_process = FastSAMPrompt(IMAGE_PATH, everything_results, device=DEVICE)
 
-# Everything prompt
-ann = prompt_process.everything_prompt()
+        # Everything prompt
+        ann = prompt_process.everything_prompt()
 
-# Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
-ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
+        # Bbox default shape [0,0,0,0] -> [x1,y1,x2,y2]
+        ann = prompt_process.box_prompt(bbox=[200, 200, 300, 300])
 
-# Text prompt
-ann = prompt_process.text_prompt(text='a photo of a dog')
+        # Text prompt
+        ann = prompt_process.text_prompt(text='a photo of a dog')
 
-# Point prompt
-# points default [[0,0]] [[x1,y1],[x2,y2]]
-# point_label default [0] [1,0] 0:background, 1:foreground
-ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
-prompt_process.plot(annotations=ann, output='./')
-```
+        # Point prompt
+        # points default [[0,0]] [[x1,y1],[x2,y2]]
+        # point_label default [0] [1,0] 0:background, 1:foreground
+        ann = prompt_process.point_prompt(points=[[200, 200]], pointlabel=[1])
+        prompt_process.plot(annotations=ann, output='./')
+        ```
 
 This snippet demonstrates the simplicity of loading a pre-trained model and running a prediction on an image.
 
@@ -83,15 +86,19 @@ This snippet demonstrates the simplicity of loading a pre-trained model and runn
 
 Validation of the model on a dataset can be done as follows:
 
-```python
-from ultralytics import FastSAM
+!!! example ""
 
-# Create a FastSAM model
-model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
+    === "Python"
 
-# Validate the model
-results = model.val(data='coco8-seg.yaml')
-```
+        ```python
+        from ultralytics import FastSAM
+
+        # Create a FastSAM model
+        model = FastSAM('FastSAM-s.pt')  # or FastSAM-x.pt
+
+        # Validate the model
+        results = model.val(data='coco8-seg.yaml')
+        ```
 
 Please note that FastSAM only supports detection and segmentation of a single class of object. This means it will recognize and segment all objects as the same class. Therefore, when preparing the dataset, you need to convert all object category IDs to 0.
 

docs/models/index.md

@@ -26,7 +26,7 @@ You can use many of these models directly in the Command Line Interface (CLI) or
 
 ## Usage
 
-    This example provides simple inference code for YOLO, SAM and RTDETR models. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using models with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
+This example provides simple inference code for YOLO, SAM and RTDETR models. For more options including handling inference results see [Predict](../modes/predict.md) mode. For using models with additional modes see [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md).
 
 !!! example ""
 

docs/models/mobile-sam.md

@@ -61,27 +61,33 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
 
 ### Point Prompt
 
-```python
-from ultralytics import SAM
+!!! example ""
 
-# Load the model
-model = SAM('mobile_sam.pt')
-
-# Predict a segment based on a point prompt
-model.predict('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
-```
+    === "Python"
+        ```python
+        from ultralytics import SAM
+        
+        # Load the model
+        model = SAM('mobile_sam.pt')
+        
+        # Predict a segment based on a point prompt
+        model.predict('ultralytics/assets/zidane.jpg', points=[900, 370], labels=[1])
+        ```
 
 ### Box Prompt
 
-```python
-from ultralytics import SAM
+!!! example ""
 
-# Load the model
-model = SAM('mobile_sam.pt')
-
-# Predict a segment based on a box prompt
-model.predict('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
-```
+    === "Python"
+        ```python
+        from ultralytics import SAM
+        
+        # Load the model
+        model = SAM('mobile_sam.pt')
+        
+        # Predict a segment based on a box prompt
+        model.predict('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
+        ```
 
 We have implemented `MobileSAM` and `SAM` using the same API. For more usage information, please see the [SAM page](./sam.md).
 

docs/models/sam.md

@@ -152,29 +152,33 @@ This comparison shows the order-of-magnitude differences in the model sizes and
 
 Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test:
 
-```python
-from ultralytics import FastSAM, SAM, YOLO
 
-# Profile SAM-b
-model = SAM('sam_b.pt')
-model.info()
-model('ultralytics/assets')
+!!! example ""
 
-# Profile MobileSAM
-model = SAM('mobile_sam.pt')
-model.info()
-model('ultralytics/assets')
-
-# Profile FastSAM-s
-model = FastSAM('FastSAM-s.pt')
-model.info()
-model('ultralytics/assets')
-
-# Profile YOLOv8n-seg
-model = YOLO('yolov8n-seg.pt')
-model.info()
-model('ultralytics/assets')
-```
+    === "Python"
+        ```python
+        from ultralytics import FastSAM, SAM, YOLO
+        
+        # Profile SAM-b
+        model = SAM('sam_b.pt')
+        model.info()
+        model('ultralytics/assets')
+        
+        # Profile MobileSAM
+        model = SAM('mobile_sam.pt')
+        model.info()
+        model('ultralytics/assets')
+        
+        # Profile FastSAM-s
+        model = FastSAM('FastSAM-s.pt')
+        model.info()
+        model('ultralytics/assets')
+        
+        # Profile YOLOv8n-seg
+        model = YOLO('yolov8n-seg.pt')
+        model.info()
+        model('ultralytics/assets')
+        ```
 
 ## Auto-Annotation: A Quick Path to Segmentation Datasets
 
@@ -184,11 +188,14 @@ Auto-annotation is a key feature of SAM, allowing users to generate a [segmentat
 
 To auto-annotate your dataset with the Ultralytics framework, use the `auto_annotate` function as shown below:
 
-```python
-from ultralytics.data.annotator import auto_annotate
+!!! example ""
 
-auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
-```
+    === "Python"
+        ```python
+        from ultralytics.data.annotator import auto_annotate
+        
+        auto_annotate(data="path/to/images", det_model="yolov8x.pt", sam_model='sam_b.pt')
+        ```
 
 | Argument   | Type                | Description                                                                                             | Default      |
 |------------|---------------------|---------------------------------------------------------------------------------------------------------|--------------|