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README.md

@@ -5,7 +5,7 @@
 
 Welcome to the [Ultralytics xView YOLOv3](https://github.com/ultralytics/xview-yolov3) repository! Here we provide code to train the powerful YOLOv3 object detection model on the xView dataset for the [xView Challenge](https://challenge.xviewdataset.org/). This challenge focuses on detecting objects from satellite imagery, advancing the state of the art in computer vision applications for remote sensing.
 
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detect.py

@@ -230,6 +230,8 @@ def detect(opt):
 
 
 class ConvNetb(nn.Module):
+    """Defines a convolutional neural network model with configurable classes and multiple convolutional layers."""
+
     def __init__(self, num_classes=60):
         """Initializes a ConvNetb model with configurable number of classes, defaulting to 60, and a series of
         convolutional layers.

models.py

@@ -77,7 +77,7 @@ def __init__(self):
 
 
 class YOLOLayer(nn.Module):
-    # YOLO Layer 0
+    """Processes YOLO detection layers with specified anchors, classes, and image dimensions for object detection."""
 
     def __init__(self, anchors, nC, img_dim, anchor_idxs):
         """Initializes YOLO layer with given anchors, number of classes, image dimensions, and anchor indexes."""

scoring/evaluation.py

@@ -22,6 +22,7 @@ def compute_statistics_given_rectangle_matches(groundtruth_rects_matched, rects_
         rects_matched: the rects_matched represents a list of integers returned
           from the Matching class instance to indicate the matched rectangle
           indices from groundtruth_rects for each of the rects.
+
     Returns:
         A dictionary holding the computed statistics as well as the inputs.
     """
@@ -47,6 +48,7 @@ def compute_precision_recall_given_image_statistics_list(iou_threshold, image_st
         image_statistics_list: a list of the statistics computed and returned
         by the compute_statistics_given_rectangle_matches method for a list of
         images.
+
     Returns:
         A dictionary holding the precision, recall as well as the inputs.
     """
@@ -77,6 +79,7 @@ def compute_average_precision_recall_given_precision_recall_dict(precision_recal
         compute_precision_recall_given_image_statistics_list method, which is
         calculated under a range of iou_thresholds, where the iou_threshold is
         the key.
+
     Returns:
         average_precision, average_recall.
     """
@@ -130,15 +133,16 @@ def compute_average_precision_recall(groundtruth_coordinates, coordinates, iou_t
         iou_threshold_range: the IOU threshold range to compute the average
           precision (AP) and average recall (AR). For example:
           iou_threshold_range = [0.50:0.05:0.95]
+
     Returns:
         average_precision, average_recall, as well as the precision_recall_dict,
         where precision_recall_dict holds the full precision/recall information
         for each of the iou_threshold in the iou_threshold_range.
+
     Raises:
         ValueError: if the input groundtruth_info_dict and test_info_dict show
         inconsistent information.
     """
-
     # Start to build up the Matching instances for each of the image_id_*, which
     # is to hold the IOU computation between the rectangle pairs for the same
     # image_id_*.

scoring/matching.py

@@ -30,6 +30,7 @@ def __init__(self, groundtruth_rects, rects):
         Args:
           groundtruth_rects: a list of groundtruth rectangles.
           rects: a list of rectangles to be matched against the groundtruth_rects.
+
         Raises:
           ValueError: if any item inside the groundtruth_rects or rects are not
           Rectangle type.
@@ -115,6 +116,7 @@ def cartesian(arrays, out=None):
         1-D arrays to form the cartesian product of.
     out : ndarray
         Array to place the cartesian product in.
+
     Returns
     -------
     out : ndarray

scoring/rectangle.py

@@ -69,7 +69,6 @@ def area(self):
 
     def intersect(self, other):
         """Returns the intersection of this rectangle with the other rectangle."""
-
         xmin = max(self.xmin_, other.xmin_)
         ymin = max(self.ymin_, other.ymin_)
         xmax = min(self.xmax_, other.xmax_)

utils/datasets.py

@@ -13,6 +13,8 @@
 
 
 class ImageFolder:  # for eval-only
+    """Loads and preprocesses images from a directory or file for evaluation with batch processing and resizing."""
+
     def __init__(self, path, batch_size=1, img_size=416):
         """Initializes an ImageFolder object to load images from directory or file for evaluation, with customizable
         batch size and image size.
@@ -61,6 +63,8 @@ def __len__(self):
 
 
 class ListDataset:  # for training
+    """Loads and processes images and their corresponding labels for training from a specified directory."""
+
     def __init__(self, path, batch_size=1, img_size=608, targets_path=""):
         """Initializes ListDataset for image training with optional batch size and target path, ensuring image path
         contains images.

utils/utils.py

@@ -402,7 +402,6 @@ def compute_ap(recall, precision):
 def bbox_iou(box1, box2, x1y1x2y2=True):
     # if len(box1.shape) == 1:
     #    box1 = box1.reshape(1, 4)
-
     """Returns the IoU of two bounding boxes."""
     if x1y1x2y2:
         # Get the coordinates of bounding boxes
@@ -518,7 +517,6 @@ def build_targets(pred_boxes, pred_conf, pred_cls, target, anchor_wh, nA, nC, nG
 
 def non_max_suppression(prediction, conf_thres=0.5, nms_thres=0.4, mat=None, img=None, model2=None, device="cpu"):
     """Performs Non-Maximum Suppression on detection results, filtering out low-confidence detections."""
-
     prediction = prediction.cpu()
     output = [None for _ in range(len(prediction))]
     # Gather bbox priors
@@ -714,7 +712,6 @@ def secondary_class_detection(x, y, w, h, img, model, device):
 
 def createChips():
     """Generates and saves a dataset of image chips from the xview dataset for classifier training."""
-
     from sys import platform
 
     import cv2