Reformat Markdown code blocks (#12795)

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

docs/en/yolov5/tutorials/architecture_description.md

@@ -32,6 +32,7 @@ To test the speed of `SPP` and `SPPF`, the following code can be used:
 
 ```python
 import time
+
 import torch
 import torch.nn as nn
 
@@ -87,7 +88,7 @@ def main():
     print(f"SPPF time: {time.time() - t_start}")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     main()
 ```
 

docs/en/yolov5/tutorials/model_export.md

@@ -181,20 +181,20 @@ Use PyTorch Hub with exported YOLOv5 models:
 import torch
 
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.torchscript ')  # TorchScript
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx')  # ONNX Runtime
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s_openvino_model')  # OpenVINO
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.engine')  # TensorRT
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.mlmodel')  # CoreML (macOS Only)
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s_saved_model')  # TensorFlow SavedModel
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pb')  # TensorFlow GraphDef
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.tflite')  # TensorFlow Lite
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s_edgetpu.tflite')  # TensorFlow Edge TPU
-model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s_paddle_model')  # PaddlePaddle
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.pt")
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.torchscript ")  # TorchScript
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.onnx")  # ONNX Runtime
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_openvino_model")  # OpenVINO
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.engine")  # TensorRT
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.mlmodel")  # CoreML (macOS Only)
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_saved_model")  # TensorFlow SavedModel
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.pb")  # TensorFlow GraphDef
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s.tflite")  # TensorFlow Lite
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_edgetpu.tflite")  # TensorFlow Edge TPU
+model = torch.hub.load("ultralytics/yolov5", "custom", "yolov5s_paddle_model")  # PaddlePaddle
 
 # Images
-img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list
+img = "https://ultralytics.com/images/zidane.jpg"  # or file, Path, PIL, OpenCV, numpy, list
 
 # Inference
 results = model(img)

docs/en/yolov5/tutorials/neural_magic_pruning_quantization.md

@@ -135,14 +135,16 @@ deepsparse.server \
 An example request, using Python's `requests` package:
 
 ```python
-import requests, json
+import json
+
+import requests
 
 # list of images for inference (local files on client side)
-path = ['basilica.jpg']
-files = [('request', open(img, 'rb')) for img in path]
+path = ["basilica.jpg"]
+files = [("request", open(img, "rb")) for img in path]
 
 # send request over HTTP to /predict/from_files endpoint
-url = 'http://0.0.0.0:5543/predict/from_files'
+url = "http://0.0.0.0:5543/predict/from_files"
 resp = requests.post(url=url, files=files)
 
 # response is returned in JSON

docs/en/yolov5/tutorials/pytorch_hub_model_loading.md

@@ -26,10 +26,10 @@ This example loads a pretrained YOLOv5s model from PyTorch Hub as `model` and pa
 import torch
 
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")
 
 # Image
-im = 'https://ultralytics.com/images/zidane.jpg'
+im = "https://ultralytics.com/images/zidane.jpg"
 
 # Inference
 results = model(im)
@@ -52,13 +52,13 @@ import torch
 from PIL import Image
 
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")
 
 # Images
-for f in 'zidane.jpg', 'bus.jpg':
-    torch.hub.download_url_to_file('https://ultralytics.com/images/' + f, f)  # download 2 images
-im1 = Image.open('zidane.jpg')  # PIL image
-im2 = cv2.imread('bus.jpg')[..., ::-1]  # OpenCV image (BGR to RGB)
+for f in "zidane.jpg", "bus.jpg":
+    torch.hub.download_url_to_file("https://ultralytics.com/images/" + f, f)  # download 2 images
+im1 = Image.open("zidane.jpg")  # PIL image
+im2 = cv2.imread("bus.jpg")[..., ::-1]  # OpenCV image (BGR to RGB)
 
 # Inference
 results = model([im1, im2], size=640)  # batch of images
@@ -110,7 +110,7 @@ model.to(device)  # i.e. device=torch.device(0)
 Models can also be created directly on any `device`:
 
 ```python
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', device='cpu')  # load on CPU
+model = torch.hub.load("ultralytics/yolov5", "yolov5s", device="cpu")  # load on CPU
 ```
 
 💡 ProTip: Input images are automatically transferred to the correct model device before inference.
@@ -120,7 +120,7 @@ model = torch.hub.load('ultralytics/yolov5', 'yolov5s', device='cpu')  # load on
 Models can be loaded silently with `_verbose=False`:
 
 ```python
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', _verbose=False)  # load silently
+model = torch.hub.load("ultralytics/yolov5", "yolov5s", _verbose=False)  # load silently
 ```
 
 ### Input Channels
@@ -128,7 +128,7 @@ model = torch.hub.load('ultralytics/yolov5', 'yolov5s', _verbose=False)  # load
 To load a pretrained YOLOv5s model with 4 input channels rather than the default 3:
 
 ```python
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', channels=4)
+model = torch.hub.load("ultralytics/yolov5", "yolov5s", channels=4)
 ```
 
 In this case the model will be composed of pretrained weights **except for** the very first input layer, which is no longer the same shape as the pretrained input layer. The input layer will remain initialized by random weights.
@@ -138,7 +138,7 @@ In this case the model will be composed of pretrained weights **except for** the
 To load a pretrained YOLOv5s model with 10 output classes rather than the default 80:
 
 ```python
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', classes=10)
+model = torch.hub.load("ultralytics/yolov5", "yolov5s", classes=10)
 ```
 
 In this case the model will be composed of pretrained weights **except for** the output layers, which are no longer the same shape as the pretrained output layers. The output layers will remain initialized by random weights.
@@ -148,7 +148,7 @@ In this case the model will be composed of pretrained weights **except for** the
 If you run into problems with the above steps, setting `force_reload=True` may help by discarding the existing cache and force a fresh download of the latest YOLOv5 version from PyTorch Hub.
 
 ```python
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True)  # force reload
+model = torch.hub.load("ultralytics/yolov5", "yolov5s", force_reload=True)  # force reload
 ```
 
 ### Screenshot Inference
@@ -160,7 +160,7 @@ import torch
 from PIL import ImageGrab
 
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")
 
 # Image
 im = ImageGrab.grab()  # take a screenshot
@@ -174,9 +174,10 @@ results = model(im)
 YOLOv5 models can be loaded to multiple GPUs in parallel with threaded inference:
 
 ```python
-import torch
 import threading
 
+import torch
+
 
 def run(model, im):
     """Performs inference on an image using a given model and saves the output; model must support `.save()` method."""
@@ -185,12 +186,12 @@ def run(model, im):
 
 
 # Models
-model0 = torch.hub.load('ultralytics/yolov5', 'yolov5s', device=0)
-model1 = torch.hub.load('ultralytics/yolov5', 'yolov5s', device=1)
+model0 = torch.hub.load("ultralytics/yolov5", "yolov5s", device=0)
+model1 = torch.hub.load("ultralytics/yolov5", "yolov5s", device=1)
 
 # Inference
-threading.Thread(target=run, args=[model0, 'https://ultralytics.com/images/zidane.jpg'], daemon=True).start()
-threading.Thread(target=run, args=[model1, 'https://ultralytics.com/images/bus.jpg'], daemon=True).start()
+threading.Thread(target=run, args=[model0, "https://ultralytics.com/images/zidane.jpg"], daemon=True).start()
+threading.Thread(target=run, args=[model1, "https://ultralytics.com/images/bus.jpg"], daemon=True).start()
 ```
 
 ### Training
@@ -200,8 +201,8 @@ To load a YOLOv5 model for training rather than inference, set `autoshape=False`
 ```python
 import torch
 
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)  # load pretrained
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False, pretrained=False)  # load scratch
+model = torch.hub.load("ultralytics/yolov5", "yolov5s", autoshape=False)  # load pretrained
+model = torch.hub.load("ultralytics/yolov5", "yolov5s", autoshape=False, pretrained=False)  # load scratch
 ```
 
 ### Base64 Results
@@ -217,7 +218,7 @@ for im in results.ims:
     buffered = BytesIO()
     im_base64 = Image.fromarray(im)
     im_base64.save(buffered, format="JPEG")
-    print(base64.b64encode(buffered.getvalue()).decode('utf-8'))  # base64 encoded image with results
+    print(base64.b64encode(buffered.getvalue()).decode("utf-8"))  # base64 encoded image with results
 ```
 
 ### Cropped Results
@@ -258,7 +259,7 @@ Results can be sorted by column, i.e. to sort license plate digit detection left
 
 ```python
 results = model(im)  # inference
-results.pandas().xyxy[0].sort_values('xmin')  # sorted left-right
+results.pandas().xyxy[0].sort_values("xmin")  # sorted left-right
 ```
 
 ### Box-Cropped Results
@@ -332,8 +333,8 @@ This example loads a custom 20-class [VOC](https://github.com/ultralytics/yolov5
 ```python
 import torch
 
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='path/to/best.pt')  # local model
-model = torch.hub.load('path/to/yolov5', 'custom', path='path/to/best.pt', source='local')  # local repo
+model = torch.hub.load("ultralytics/yolov5", "custom", path="path/to/best.pt")  # local model
+model = torch.hub.load("path/to/yolov5", "custom", path="path/to/best.pt", source="local")  # local repo
 ```
 
 ## TensorRT, ONNX and OpenVINO Models
@@ -346,14 +347,14 @@ PyTorch Hub supports inference on most YOLOv5 export formats, including custom t
 ```python
 import torch
 
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.pt')  # PyTorch
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.torchscript')  # TorchScript
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.onnx')  # ONNX
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s_openvino_model/')  # OpenVINO
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.engine')  # TensorRT
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.mlmodel')  # CoreML (macOS-only)
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s.tflite')  # TFLite
-model = torch.hub.load('ultralytics/yolov5', 'custom', path='yolov5s_paddle_model/')  # PaddlePaddle
+model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.pt")  # PyTorch
+model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.torchscript")  # TorchScript
+model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.onnx")  # ONNX
+model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s_openvino_model/")  # OpenVINO
+model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.engine")  # TensorRT
+model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.mlmodel")  # CoreML (macOS-only)
+model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s.tflite")  # TFLite
+model = torch.hub.load("ultralytics/yolov5", "custom", path="yolov5s_paddle_model/")  # PaddlePaddle
 ```
 
 ## Supported Environments

docs/en/yolov5/tutorials/test_time_augmentation.md

@@ -131,10 +131,10 @@ TTA is automatically integrated into all [YOLOv5 PyTorch Hub](https://pytorch.or
 import torch
 
 # Model
-model = torch.hub.load('ultralytics/yolov5', 'yolov5s')  # or yolov5m, yolov5x, custom
+model = torch.hub.load("ultralytics/yolov5", "yolov5s")  # or yolov5m, yolov5x, custom
 
 # Images
-img = 'https://ultralytics.com/images/zidane.jpg'  # or file, PIL, OpenCV, numpy, multiple
+img = "https://ultralytics.com/images/zidane.jpg"  # or file, PIL, OpenCV, numpy, multiple
 
 # Inference
 results = model(img, augment=True)  # <--- TTA inference

docs/en/yolov5/tutorials/train_custom_data.md

@@ -192,7 +192,7 @@ Results file `results.csv` is updated after each epoch, and then plotted as `res
 ```python
 from utils.plots import plot_results
 
-plot_results('path/to/results.csv')  # plot 'results.csv' as 'results.png'
+plot_results("path/to/results.csv")  # plot 'results.csv' as 'results.png'
 ```
 
 <p align="center"><img width="800" alt="results.png" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/results.png"></p>

docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md

@@ -22,11 +22,11 @@ All layers that match the train.py `freeze` list in train.py will be frozen by s
 
 ```python
 # Freeze
-freeze = [f'model.{x}.' for x in range(freeze)]  # layers to freeze
+freeze = [f"model.{x}." for x in range(freeze)]  # layers to freeze
 for k, v in model.named_parameters():
     v.requires_grad = True  # train all layers
     if any(x in k for x in freeze):
-        print(f'freezing {k}')
+        print(f"freezing {k}")
         v.requires_grad = False
 ```