Update README.md (#13283)

* Update README.md

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

* Auto-format by https://ultralytics.com/actions

---------

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

export.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLOv5 🚀, AGPL-3.0 license
 """
-Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit
+Export a YOLOv5 PyTorch model to other formats. TensorFlow exports authored by https://github.com/zldrobit.
 
 Format                      | `export.py --include`         | Model
 ---                         | ---                           | ---
@@ -141,7 +141,7 @@ def forward(self, x):
 
 
 def export_formats():
-    """
+    r"""
     Returns a DataFrame of supported YOLOv5 model export formats and their properties.
 
     Returns:

hubconf.py

@@ -1,6 +1,6 @@
 # Ultralytics YOLOv5 🚀, AGPL-3.0 license
 """
-PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5
+PyTorch Hub models https://pytorch.org/hub/ultralytics_yolov5.
 
 Usage:
     import torch

models/tf.py

@@ -1,7 +1,7 @@
 # Ultralytics YOLOv5 🚀, AGPL-3.0 license
 """
 TensorFlow, Keras and TFLite versions of YOLOv5
-Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127
+Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127.
 
 Usage:
     $ python models/tf.py --weights yolov5s.pt

segment/val.py

@@ -121,7 +121,7 @@ def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, over
         detections (array[N, 6]), x1, y1, x2, y2, conf, class
         labels (array[M, 5]), class, x1, y1, x2, y2
     Returns:
-        correct (array[N, 10]), for 10 IoU levels
+        correct (array[N, 10]), for 10 IoU levels.
     """
     if masks:
         if overlap:

utils/dataloaders.py

@@ -93,7 +93,7 @@ def exif_size(img):
 def exif_transpose(image):
     """
     Transpose a PIL image accordingly if it has an EXIF Orientation tag.
-    Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose()
+    Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose().
 
     :param image: The image to transpose.
     :return: An image.
@@ -316,7 +316,7 @@ def __next__(self):
 
 
 class LoadImages:
-    """YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`"""
+    """YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`."""
 
     def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
         """Initializes YOLOv5 loader for images/videos, supporting glob patterns, directories, and lists of paths."""
@@ -1103,7 +1103,7 @@ def extract_boxes(path=DATASETS_DIR / "coco128"):
 
 def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False):
     """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
-    Usage: from utils.dataloaders import *; autosplit()
+    Usage: from utils.dataloaders import *; autosplit().
 
     Arguments:
         path:            Path to images directory

utils/loggers/__init__.py

@@ -349,7 +349,7 @@ def on_params_update(self, params: dict):
 class GenericLogger:
     """
     YOLOv5 General purpose logger for non-task specific logging
-    Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...)
+    Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...).
 
     Arguments:
         opt:             Run arguments

utils/loggers/clearml/clearml_utils.py

@@ -78,7 +78,7 @@ class ClearmlLogger:
     def __init__(self, opt, hyp):
         """
         - Initialize ClearML Task, this object will capture the experiment
-        - Upload dataset version to ClearML Data if opt.upload_dataset is True
+        - Upload dataset version to ClearML Data if opt.upload_dataset is True.
 
         Arguments:
         opt (namespace) -- Commandline arguments for this run

utils/loggers/wandb/wandb_utils.py

@@ -47,7 +47,7 @@ def __init__(self, opt, run_id=None, job_type="Training"):
         """
         - Initialize WandbLogger instance
         - Upload dataset if opt.upload_dataset is True
-        - Setup training processes if job_type is 'Training'
+        - Setup training processes if job_type is 'Training'.
 
         Arguments:
         opt (namespace) -- Commandline arguments for this run
@@ -88,7 +88,7 @@ def setup_training(self, opt):
         Setup the necessary processes for training YOLO models:
           - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
           - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
-          - Setup log_dict, initialize bbox_interval
+          - Setup log_dict, initialize bbox_interval.
 
         Arguments:
         opt (namespace) -- commandline arguments for this run

utils/loss.py

@@ -9,7 +9,7 @@
 
 
 def smooth_BCE(eps=0.1):
-    """Returns label smoothing BCE targets for reducing overfitting; pos: `1.0 - 0.5*eps`, neg: `0.5*eps`. For details see https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441"""
+    """Returns label smoothing BCE targets for reducing overfitting; pos: `1.0 - 0.5*eps`, neg: `0.5*eps`. For details see https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441."""
     return 1.0 - 0.5 * eps, 0.5 * eps
 
 

utils/metrics.py

@@ -100,7 +100,7 @@ def compute_ap(recall, precision):
         recall:    The recall curve (list)
         precision: The precision curve (list)
     # Returns
-        Average precision, precision curve, recall curve
+        Average precision, precision curve, recall curve.
     """
     # Append sentinel values to beginning and end
     mrec = np.concatenate(([0.0], recall, [1.0]))

utils/plots.py

@@ -81,7 +81,7 @@ def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detec
     module_type:    Module type
     stage:          Module stage within model
     n:              Maximum number of feature maps to plot
-    save_dir:       Directory to save results
+    save_dir:       Directory to save results.
     """
     if ("Detect" not in module_type) and (
         "Segment" not in module_type

utils/segment/general.py

@@ -28,7 +28,7 @@ def process_mask_upsample(protos, masks_in, bboxes, shape):
     protos: [mask_dim, mask_h, mask_w]
     masks_in: [n, mask_dim], n is number of masks after nms
     bboxes: [n, 4], n is number of masks after nms
-    shape: input_image_size, (h, w)
+    shape: input_image_size, (h, w).
 
     return: h, w, n
     """
@@ -45,7 +45,7 @@ def process_mask(protos, masks_in, bboxes, shape, upsample=False):
     proto_out: [mask_dim, mask_h, mask_w]
     out_masks: [n, mask_dim], n is number of masks after nms
     bboxes: [n, 4], n is number of masks after nms
-    shape:input_image_size, (h, w)
+    shape:input_image_size, (h, w).
 
     return: h, w, n
     """
@@ -71,7 +71,7 @@ def process_mask_native(protos, masks_in, bboxes, shape):
     protos: [mask_dim, mask_h, mask_w]
     masks_in: [n, mask_dim], n is number of masks after nms
     bboxes: [n, 4], n is number of masks after nms
-    shape: input_image_size, (h, w)
+    shape: input_image_size, (h, w).
 
     return: h, w, n
     """
@@ -92,7 +92,7 @@ def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
     """
     img1_shape: model input shape, [h, w]
     img0_shape: origin pic shape, [h, w, 3]
-    masks: [h, w, num]
+    masks: [h, w, num].
     """
     # Rescale coordinates (xyxy) from im1_shape to im0_shape
     if ratio_pad is None:  # calculate from im0_shape
@@ -120,7 +120,7 @@ def mask_iou(mask1, mask2, eps=1e-7):
     """
     mask1: [N, n] m1 means number of predicted objects
     mask2: [M, n] m2 means number of gt objects
-    Note: n means image_w x image_h
+    Note: n means image_w x image_h.
 
     return: masks iou, [N, M]
     """
@@ -133,7 +133,7 @@ def masks_iou(mask1, mask2, eps=1e-7):
     """
     mask1: [N, n] m1 means number of predicted objects
     mask2: [N, n] m2 means number of gt objects
-    Note: n means image_w x image_h
+    Note: n means image_w x image_h.
 
     return: masks iou, (N, )
     """

utils/segment/metrics.py

@@ -127,7 +127,7 @@ def mean_results(self):
         return (self.mp, self.mr, self.map50, self.map)
 
     def class_result(self, i):
-        """Class-aware result, return p[i], r[i], ap50[i], ap[i]"""
+        """Class-aware result, return p[i], r[i], ap50[i], ap[i]."""
         return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
 
     def get_maps(self, nc):
@@ -140,7 +140,7 @@ def get_maps(self, nc):
     def update(self, results):
         """
         Args:
-            results: tuple(p, r, ap, f1, ap_class)
+            results: tuple(p, r, ap, f1, ap_class).
         """
         p, r, all_ap, f1, ap_class_index = results
         self.p = p
@@ -163,7 +163,7 @@ def __init__(self) -> None:
     def update(self, results):
         """
         Args:
-            results: Dict{'boxes': Dict{}, 'masks': Dict{}}
+            results: Dict{'boxes': Dict{}, 'masks': Dict{}}.
         """
         self.metric_box.update(list(results["boxes"].values()))
         self.metric_mask.update(list(results["masks"].values()))

utils/torch_utils.py

@@ -161,7 +161,7 @@ def profile(input, ops, n=10, device=None):
         input = torch.randn(16, 3, 640, 640)
         m1 = lambda x: x * torch.sigmoid(x)
         m2 = nn.SiLU()
-        profile(input, [m1, m2], n=100)  # profile over 100 iterations
+        profile(input, [m1, m2], n=100)  # profile over 100 iterations.
     """
     results = []
     if not isinstance(device, torch.device):
@@ -449,7 +449,7 @@ def __call__(self, epoch, fitness):
 class ModelEMA:
     """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
     Keeps a moving average of everything in the model state_dict (parameters and buffers)
-    For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
+    For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage.
     """
 
     def __init__(self, model, decay=0.9999, tau=2000, updates=0):

utils/triton.py

@@ -18,7 +18,7 @@ class TritonRemoteModel:
     def __init__(self, url: str):
         """
         Keyword Arguments:
-        url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000
+        url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000.
         """
         parsed_url = urlparse(url)
         if parsed_url.scheme == "grpc":