assert best possible recall > 0.9 before training

utils/datasets.py

@@ -291,20 +291,22 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         self.label_files = [x.replace('images', 'labels').replace(os.path.splitext(x)[-1], '.txt')
                             for x in self.img_files]
 
+        # Read image shapes (wh)
+        sp = path.replace('.txt', '') + '.shapes'  # shapefile path
+        try:
+            with open(sp, 'r') as f:  # read existing shapefile
+                s = [x.split() for x in f.read().splitlines()]
+                assert len(s) == n, 'Shapefile out of sync'
+        except:
+            s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
+            np.savetxt(sp, s, fmt='%g')  # overwrites existing (if any)
+
+        self.shapes = np.array(s, dtype=np.float64)
+
         # Rectangular Training  https://github.com/ultralytics/yolov3/issues/232
         if self.rect:
-            # Read image shapes (wh)
-            sp = path.replace('.txt', '') + '.shapes'  # shapefile path
-            try:
-                with open(sp, 'r') as f:  # read existing shapefile
-                    s = [x.split() for x in f.read().splitlines()]
-                    assert len(s) == n, 'Shapefile out of sync'
-            except:
-                s = [exif_size(Image.open(f)) for f in tqdm(self.img_files, desc='Reading image shapes')]
-                np.savetxt(sp, s, fmt='%g')  # overwrites existing (if any)
-
             # Sort by aspect ratio
-            s = np.array(s, dtype=np.float64)
+            s = self.shapes  # wh
             ar = s[:, 1] / s[:, 0]  # aspect ratio
             irect = ar.argsort()
             self.img_files = [self.img_files[i] for i in irect]