Add https://www.reddit.com/r/Ultralytics/ badge (#13284)

* Refactor code for speed and clarity

* Update README.zh-CN.md

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

---------

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

models/common.py

@@ -71,7 +71,8 @@ def autopad(k, p=None, d=1):
 
 
 class Conv(nn.Module):
-    # Standard convolution with args(ch_in, ch_out, kernel, stride, padding, groups, dilation, activation)
+    """Applies a convolution, batch normalization, and activation function to an input tensor in a neural network."""
+
     default_act = nn.SiLU()  # default activation
 
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True):
@@ -91,7 +92,8 @@ class Conv(nn.Module):
 
 
 class DWConv(Conv):
-    # Depth-wise convolution
+    """Implements a depth-wise convolution layer with optional activation for efficient spatial filtering."""
+
     def __init__(self, c1, c2, k=1, s=1, d=1, act=True):
         """Initializes a depth-wise convolution layer with optional activation; args: input channels (c1), output
         channels (c2), kernel size (k), stride (s), dilation (d), and activation flag (act).
@@ -100,7 +102,8 @@ class DWConv(Conv):
 
 
 class DWConvTranspose2d(nn.ConvTranspose2d):
-    # Depth-wise transpose convolution
+    """A depth-wise transpose convolutional layer for upsampling in neural networks, particularly in YOLOv5 models."""
+
     def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0):
         """Initializes a depth-wise transpose convolutional layer for YOLOv5; args: input channels (c1), output channels
         (c2), kernel size (k), stride (s), input padding (p1), output padding (p2).
@@ -109,7 +112,8 @@ class DWConvTranspose2d(nn.ConvTranspose2d):
 
 
 class TransformerLayer(nn.Module):
-    # Transformer layer https://arxiv.org/abs/2010.11929 (LayerNorm layers removed for better performance)
+    """Transformer layer with multihead attention and linear layers, optimized by removing LayerNorm."""
+
     def __init__(self, c, num_heads):
         """
         Initializes a transformer layer, sans LayerNorm for performance, with multihead attention and linear layers.
@@ -132,7 +136,8 @@ class TransformerLayer(nn.Module):
 
 
 class TransformerBlock(nn.Module):
-    # Vision Transformer https://arxiv.org/abs/2010.11929
+    """A Transformer block for vision tasks with convolution, position embeddings, and Transformer layers."""
+
     def __init__(self, c1, c2, num_heads, num_layers):
         """Initializes a Transformer block for vision tasks, adapting dimensions if necessary and stacking specified
         layers.
@@ -157,7 +162,8 @@ class TransformerBlock(nn.Module):
 
 
 class Bottleneck(nn.Module):
-    # Standard bottleneck
+    """A bottleneck layer with optional shortcut and group convolution for efficient feature extraction."""
+
     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):
         """Initializes a standard bottleneck layer with optional shortcut and group convolution, supporting channel
         expansion.
@@ -176,7 +182,8 @@ class Bottleneck(nn.Module):
 
 
 class BottleneckCSP(nn.Module):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    """CSP bottleneck layer for feature extraction with cross-stage partial connections and optional shortcuts."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes CSP bottleneck with optional shortcuts; args: ch_in, ch_out, number of repeats, shortcut bool,
         groups, expansion.
@@ -201,7 +208,8 @@ class BottleneckCSP(nn.Module):
 
 
 class CrossConv(nn.Module):
-    # Cross Convolution Downsample
+    """Implements a cross convolution layer with downsampling, expansion, and optional shortcut."""
+
     def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False):
         """
         Initializes CrossConv with downsampling, expanding, and optionally shortcutting; `c1` input, `c2` output
@@ -221,7 +229,8 @@ class CrossConv(nn.Module):
 
 
 class C3(nn.Module):
-    # CSP Bottleneck with 3 convolutions
+    """Implements a CSP Bottleneck module with three convolutions for enhanced feature extraction in neural networks."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, group
         convolutions, and expansion.
@@ -239,7 +248,8 @@ class C3(nn.Module):
 
 
 class C3x(C3):
-    # C3 module with cross-convolutions
+    """Extends the C3 module with cross-convolutions for enhanced feature extraction in neural networks."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes C3x module with cross-convolutions, extending C3 with customizable channel dimensions, groups,
         and expansion.
@@ -250,7 +260,8 @@ class C3x(C3):
 
 
 class C3TR(C3):
-    # C3 module with TransformerBlock()
+    """C3 module with TransformerBlock for enhanced feature extraction in object detection models."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes C3 module with TransformerBlock for enhanced feature extraction, accepts channel sizes, shortcut
         config, group, and expansion.
@@ -261,7 +272,8 @@ class C3TR(C3):
 
 
 class C3SPP(C3):
-    # C3 module with SPP()
+    """Extends the C3 module with an SPP layer for enhanced spatial feature extraction and customizable channels."""
+
     def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5):
         """Initializes a C3 module with SPP layer for advanced spatial feature extraction, given channel sizes, kernel
         sizes, shortcut, group, and expansion ratio.
@@ -272,7 +284,8 @@ class C3SPP(C3):
 
 
 class C3Ghost(C3):
-    # C3 module with GhostBottleneck()
+    """Implements a C3 module with Ghost Bottlenecks for efficient feature extraction in YOLOv5."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):
         """Initializes YOLOv5's C3 module with Ghost Bottlenecks for efficient feature extraction."""
         super().__init__(c1, c2, n, shortcut, g, e)
@@ -281,7 +294,8 @@ class C3Ghost(C3):
 
 
 class SPP(nn.Module):
-    # Spatial Pyramid Pooling (SPP) layer https://arxiv.org/abs/1406.4729
+    """Implements Spatial Pyramid Pooling (SPP) for feature extraction, ref: https://arxiv.org/abs/1406.4729."""
+
     def __init__(self, c1, c2, k=(5, 9, 13)):
         """Initializes SPP layer with Spatial Pyramid Pooling, ref: https://arxiv.org/abs/1406.4729, args: c1 (input channels), c2 (output channels), k (kernel sizes)."""
         super().__init__()
@@ -301,7 +315,8 @@ class SPP(nn.Module):
 
 
 class SPPF(nn.Module):
-    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
+    """Implements a fast Spatial Pyramid Pooling (SPPF) layer for efficient feature extraction in YOLOv5 models."""
+
     def __init__(self, c1, c2, k=5):
         """
         Initializes YOLOv5 SPPF layer with given channels and kernel size for YOLOv5 model, combining convolution and
@@ -326,7 +341,8 @@ class SPPF(nn.Module):
 
 
 class Focus(nn.Module):
-    # Focus wh information into c-space
+    """Focuses spatial information into channel space using slicing and convolution for efficient feature extraction."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True):
         """Initializes Focus module to concentrate width-height info into channel space with configurable convolution
         parameters.
@@ -342,7 +358,8 @@ class Focus(nn.Module):
 
 
 class GhostConv(nn.Module):
-    # Ghost Convolution https://github.com/huawei-noah/ghostnet
+    """Implements Ghost Convolution for efficient feature extraction, see https://github.com/huawei-noah/ghostnet."""
+
     def __init__(self, c1, c2, k=1, s=1, g=1, act=True):
         """Initializes GhostConv with in/out channels, kernel size, stride, groups, and activation; halves out channels
         for efficiency.
@@ -359,7 +376,8 @@ class GhostConv(nn.Module):
 
 
 class GhostBottleneck(nn.Module):
-    # Ghost Bottleneck https://github.com/huawei-noah/ghostnet
+    """Efficient bottleneck layer using Ghost Convolutions, see https://github.com/huawei-noah/ghostnet."""
+
     def __init__(self, c1, c2, k=3, s=1):
         """Initializes GhostBottleneck with ch_in `c1`, ch_out `c2`, kernel size `k`, stride `s`; see https://github.com/huawei-noah/ghostnet."""
         super().__init__()
@@ -379,7 +397,8 @@ class GhostBottleneck(nn.Module):
 
 
 class Contract(nn.Module):
-    # Contract width-height into channels, i.e. x(1,64,80,80) to x(1,256,40,40)
+    """Contracts spatial dimensions into channel dimensions for efficient processing in neural networks."""
+
     def __init__(self, gain=2):
         """Initializes a layer to contract spatial dimensions (width-height) into channels, e.g., input shape
         (1,64,80,80) to (1,256,40,40).
@@ -399,7 +418,8 @@ class Contract(nn.Module):
 
 
 class Expand(nn.Module):
-    # Expand channels into width-height, i.e. x(1,64,80,80) to x(1,16,160,160)
+    """Expands spatial dimensions by redistributing channels, e.g., from (1,64,80,80) to (1,16,160,160)."""
+
     def __init__(self, gain=2):
         """
         Initializes the Expand module to increase spatial dimensions by redistributing channels, with an optional gain
@@ -422,7 +442,8 @@ class Expand(nn.Module):
 
 
 class Concat(nn.Module):
-    # Concatenate a list of tensors along dimension
+    """Concatenates tensors along a specified dimension for efficient tensor manipulation in neural networks."""
+
     def __init__(self, dimension=1):
         """Initializes a Concat module to concatenate tensors along a specified dimension."""
         super().__init__()
@@ -436,7 +457,8 @@ class Concat(nn.Module):
 
 
 class DetectMultiBackend(nn.Module):
-    # YOLOv5 MultiBackend class for python inference on various backends
+    """YOLOv5 MultiBackend class for inference on various backends including PyTorch, ONNX, TensorRT, and more."""
+
     def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True):
         """Initializes DetectMultiBackend with support for various inference backends, including PyTorch and ONNX."""
         #   PyTorch:              weights = *.pt
@@ -778,7 +800,8 @@ class DetectMultiBackend(nn.Module):
 
 
 class AutoShape(nn.Module):
-    # YOLOv5 input-robust model wrapper for passing cv2/np/PIL/torch inputs. Includes preprocessing, inference and NMS
+    """AutoShape class for robust YOLOv5 inference with preprocessing, NMS, and support for various input formats."""
+
     conf = 0.25  # NMS confidence threshold
     iou = 0.45  # NMS IoU threshold
     agnostic = False  # NMS class-agnostic
@@ -889,7 +912,8 @@ class AutoShape(nn.Module):
 
 
 class Detections:
-    # YOLOv5 detections class for inference results
+    """Manages YOLOv5 detection results with methods for visualization, saving, cropping, and exporting detections."""
+
     def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None):
         """Initializes the YOLOv5 Detections class with image info, predictions, filenames, timing and normalization."""
         super().__init__()
@@ -1047,7 +1071,8 @@ class Detections:
 
 
 class Proto(nn.Module):
-    # YOLOv5 mask Proto module for segmentation models
+    """YOLOv5 mask Proto module for segmentation models, performing convolutions and upsampling on input tensors."""
+
     def __init__(self, c1, c_=256, c2=32):
         """Initializes YOLOv5 Proto module for segmentation with input, proto, and mask channels configuration."""
         super().__init__()
@@ -1062,7 +1087,8 @@ class Proto(nn.Module):
 
 
 class Classify(nn.Module):
-    # YOLOv5 classification head, i.e. x(b,c1,20,20) to x(b,c2)
+    """YOLOv5 classification head with convolution, pooling, and dropout layers for channel transformation."""
+
     def __init__(
         self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0
     ):  # ch_in, ch_out, kernel, stride, padding, groups, dropout probability

models/tf.py

@@ -49,7 +49,8 @@ from utils.general import LOGGER, make_divisible, print_args
 
 
 class TFBN(keras.layers.Layer):
-    # TensorFlow BatchNormalization wrapper
+    """TensorFlow BatchNormalization wrapper for initializing with optional pretrained weights."""
+
     def __init__(self, w=None):
         """Initializes a TensorFlow BatchNormalization layer with optional pretrained weights."""
         super().__init__()
@@ -67,7 +68,8 @@ class TFBN(keras.layers.Layer):
 
 
 class TFPad(keras.layers.Layer):
-    # Pad inputs in spatial dimensions 1 and 2
+    """Pads input tensors in spatial dimensions 1 and 2 with specified integer or tuple padding values."""
+
     def __init__(self, pad):
         """
         Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple
@@ -87,7 +89,8 @@ class TFPad(keras.layers.Layer):
 
 
 class TFConv(keras.layers.Layer):
-    # Standard convolution
+    """Implements a standard convolutional layer with optional batch normalization and activation for TensorFlow."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
         """
         Initializes a standard convolution layer with optional batch normalization and activation; supports only
@@ -118,7 +121,8 @@ class TFConv(keras.layers.Layer):
 
 
 class TFDWConv(keras.layers.Layer):
-    # Depthwise convolution
+    """Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None):
         """
         Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow
@@ -147,7 +151,8 @@ class TFDWConv(keras.layers.Layer):
 
 
 class TFDWConvTranspose2d(keras.layers.Layer):
-    # Depthwise ConvTranspose2d
+    """Implements a depthwise ConvTranspose2D layer for TensorFlow with specific settings."""
+
     def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None):
         """
         Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings.
@@ -179,7 +184,8 @@ class TFDWConvTranspose2d(keras.layers.Layer):
 
 
 class TFFocus(keras.layers.Layer):
-    # Focus wh information into c-space
+    """Focuses spatial information into channel space using pixel shuffling and convolution for TensorFlow models."""
+
     def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None):
         """
         Initializes TFFocus layer to focus width and height information into channel space with custom convolution
@@ -201,7 +207,8 @@ class TFFocus(keras.layers.Layer):
 
 
 class TFBottleneck(keras.layers.Layer):
-    # Standard bottleneck
+    """Implements a TensorFlow bottleneck layer with optional shortcut connections for efficient feature extraction."""
+
     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None):
         """
         Initializes a standard bottleneck layer for TensorFlow models, expanding and contracting channels with optional
@@ -223,7 +230,8 @@ class TFBottleneck(keras.layers.Layer):
 
 
 class TFCrossConv(keras.layers.Layer):
-    # Cross Convolution
+    """Implements a cross convolutional layer with optional expansion, grouping, and shortcut for TensorFlow."""
+
     def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None):
         """Initializes cross convolution layer with optional expansion, grouping, and shortcut addition capabilities."""
         super().__init__()
@@ -238,7 +246,8 @@ class TFCrossConv(keras.layers.Layer):
 
 
 class TFConv2d(keras.layers.Layer):
-    # Substitution for PyTorch nn.Conv2D
+    """Implements a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D for specified filters and stride."""
+
     def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None):
         """Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter
         sizes and stride.
@@ -261,7 +270,8 @@ class TFConv2d(keras.layers.Layer):
 
 
 class TFBottleneckCSP(keras.layers.Layer):
-    # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks
+    """Implements a CSP bottleneck layer for TensorFlow models to enhance gradient flow and efficiency."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
         """
         Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion
@@ -289,7 +299,8 @@ class TFBottleneckCSP(keras.layers.Layer):
 
 
 class TFC3(keras.layers.Layer):
-    # CSP Bottleneck with 3 convolutions
+    """CSP bottleneck layer with 3 convolutions for TensorFlow, supporting optional shortcuts and group convolutions."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
         """
         Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions.
@@ -313,7 +324,8 @@ class TFC3(keras.layers.Layer):
 
 
 class TFC3x(keras.layers.Layer):
-    # 3 module with cross-convolutions
+    """A TensorFlow layer for enhanced feature extraction using cross-convolutions in object detection models."""
+
     def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None):
         """
         Initializes layer with cross-convolutions for enhanced feature extraction in object detection models.
@@ -335,7 +347,8 @@ class TFC3x(keras.layers.Layer):
 
 
 class TFSPP(keras.layers.Layer):
-    # Spatial pyramid pooling layer used in YOLOv3-SPP
+    """Implements spatial pyramid pooling for YOLOv3-SPP with specific channels and kernel sizes."""
+
     def __init__(self, c1, c2, k=(5, 9, 13), w=None):
         """Initializes a YOLOv3-SPP layer with specific input/output channels and kernel sizes for pooling."""
         super().__init__()
@@ -351,7 +364,8 @@ class TFSPP(keras.layers.Layer):
 
 
 class TFSPPF(keras.layers.Layer):
-    # Spatial pyramid pooling-Fast layer
+    """Implements a fast spatial pyramid pooling layer for TensorFlow with optimized feature extraction."""
+
     def __init__(self, c1, c2, k=5, w=None):
         """Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and
         weights.
@@ -373,7 +387,8 @@ class TFSPPF(keras.layers.Layer):
 
 
 class TFDetect(keras.layers.Layer):
-    # TF YOLOv5 Detect layer
+    """Implements YOLOv5 object detection layer in TensorFlow for predicting bounding boxes and class probabilities."""
+
     def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None):
         """Initializes YOLOv5 detection layer for TensorFlow with configurable classes, anchors, channels, and image
         size.
@@ -427,7 +442,8 @@ class TFDetect(keras.layers.Layer):
 
 
 class TFSegment(TFDetect):
-    # YOLOv5 Segment head for segmentation models
+    """YOLOv5 segmentation head for TensorFlow, combining detection and segmentation."""
+
     def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None):
         """Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation
         models.
@@ -450,6 +466,8 @@ class TFSegment(TFDetect):
 
 
 class TFProto(keras.layers.Layer):
+    """Implements convolutional and upsampling layers for feature extraction in YOLOv5 segmentation."""
+
     def __init__(self, c1, c_=256, c2=32, w=None):
         """Initializes TFProto layer with convolutional and upsampling layers for feature extraction and
         transformation.
@@ -466,7 +484,8 @@ class TFProto(keras.layers.Layer):
 
 
 class TFUpsample(keras.layers.Layer):
-    # TF version of torch.nn.Upsample()
+    """Implements a TensorFlow upsampling layer with specified size, scale factor, and interpolation mode."""
+
     def __init__(self, size, scale_factor, mode, w=None):
         """
         Initializes a TensorFlow upsampling layer with specified size, scale_factor, and mode, ensuring scale_factor is
@@ -488,7 +507,8 @@ class TFUpsample(keras.layers.Layer):
 
 
 class TFConcat(keras.layers.Layer):
-    # TF version of torch.concat()
+    """Implements TensorFlow's version of torch.concat() for concatenating tensors along the last dimension."""
+
     def __init__(self, dimension=1, w=None):
         """Initializes a TensorFlow layer for NCHW to NHWC concatenation, requiring dimension=1."""
         super().__init__()
@@ -581,7 +601,8 @@ def parse_model(d, ch, model, imgsz):
 
 
 class TFModel:
-    # TF YOLOv5 model
+    """Implements YOLOv5 model in TensorFlow, supporting TensorFlow, Keras, and TFLite formats for object detection."""
+
     def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)):
         """Initializes TF YOLOv5 model with specified configuration, channels, classes, model instance, and input
         size.
@@ -653,7 +674,8 @@ class TFModel:
 
 
 class AgnosticNMS(keras.layers.Layer):
-    # TF Agnostic NMS
+    """Performs agnostic non-maximum suppression (NMS) on detected objects using IoU and confidence thresholds."""
+
     def call(self, input, topk_all, iou_thres, conf_thres):
         """Performs agnostic NMS on input tensors using given thresholds and top-K selection."""
         return tf.map_fn(

models/yolo.py

@@ -70,7 +70,8 @@ except ImportError:
 
 
 class Detect(nn.Module):
-    # YOLOv5 Detect head for detection models
+    """YOLOv5 Detect head for processing input tensors and generating detection outputs in object detection models."""
+
     stride = None  # strides computed during build
     dynamic = False  # force grid reconstruction
     export = False  # export mode
@@ -127,7 +128,8 @@ class Detect(nn.Module):
 
 
 class Segment(Detect):
-    # YOLOv5 Segment head for segmentation models
+    """YOLOv5 Segment head for segmentation models, extending Detect with mask and prototype layers."""
+
     def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True):
         """Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments."""
         super().__init__(nc, anchors, ch, inplace)
@@ -214,7 +216,8 @@ class BaseModel(nn.Module):
 
 
 class DetectionModel(BaseModel):
-    # YOLOv5 detection model
+    """YOLOv5 detection model class for object detection tasks, supporting custom configurations and anchors."""
+
     def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None):
         """Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors."""
         super().__init__()
@@ -332,14 +335,16 @@ Model = DetectionModel  # retain YOLOv5 'Model' class for backwards compatibilit
 
 
 class SegmentationModel(DetectionModel):
-    # YOLOv5 segmentation model
+    """YOLOv5 segmentation model for object detection and segmentation tasks with configurable parameters."""
+
     def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None):
         """Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list)."""
         super().__init__(cfg, ch, nc, anchors)
 
 
 class ClassificationModel(BaseModel):
-    # YOLOv5 classification model
+    """YOLOv5 classification model for image classification tasks, initialized with a config file or detection model."""
+
     def __init__(self, cfg=None, model=None, nc=1000, cutoff=10):
         """Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff`
         index.