updates

utils/datasets.py

@@ -236,7 +236,9 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         nL = len(labels)  # number of labels
         if nL:
             # convert xyxy to xywh
-            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5]) / self.img_size
+            labels[:, 1:5] = xyxy2xywh(labels[:, 1:5])
+            labels[:, [2, 4]] /= img.shape[0]  # height
+            labels[:, [1, 3]] /= img.shape[1]  # width
 
         if self.augment:
             # random left-right flip

utils/utils.py

@@ -500,13 +500,14 @@ def plot_images(imgs, targets, fname='images.jpg'):
     targets = targets.cpu().numpy()
 
     fig = plt.figure(figsize=(10, 10))
-    img_size = imgs.shape[3]
-    bs = imgs.shape[0]  # batch size
-    sp = np.ceil(bs ** 0.5)  # subplots
+    bs, _, h, w = imgs.shape  # batch size, _, height, width
+    ns = np.ceil(bs ** 0.5)  # number of subplots
 
     for i in range(bs):
-        boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T * img_size
-        plt.subplot(sp, sp, i + 1).imshow(imgs[i].transpose(1, 2, 0))
+        boxes = xywh2xyxy(targets[targets[:, 0] == i, 2:6]).T
+        boxes[[0, 2]] *= w
+        boxes[[1, 3]] *= h
+        plt.subplot(ns, ns, i + 1).imshow(imgs[i].transpose(1, 2, 0))
         plt.plot(boxes[[0, 2, 2, 0, 0]], boxes[[1, 1, 3, 3, 1]], '.-')
         plt.axis('off')
     fig.tight_layout()