add *.jpeg support

utils/datasets.py

@@ -140,15 +140,14 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
         assert n > 0, 'No images found in %s' % path
         self.img_size = img_size
         self.augment = augment
+        self.image_weights = image_weights
+        self.rect = False if image_weights else rect
         self.label_files = [x.replace('images', 'labels').
                                 replace('.jpeg', '.txt').
                                 replace('.jpg', '.txt').
                                 replace('.bmp', '.txt').
                                 replace('.png', '.txt') for x in self.img_files]
 
-        self.image_weights = image_weights
-        self.rect = False if image_weights else rect
-
         # Rectangular Training  https://github.com/ultralytics/yolov3/issues/232
         if self.rect:
             from PIL import Image
@@ -187,7 +186,7 @@ class LoadImagesAndLabels(Dataset):  # for training/testing
 
         # Preload images
         if n < 1001:  # preload all images into memory if possible
-            self.imgs = [cv2.imread(self.img_files[i]) for i in range(n)]
+            self.imgs = [cv2.imread(self.img_files[i]) for i in tqdm(range(n), desc='Reading images')]
 
         # Preload labels (required for weighted CE training)
         self.labels = [np.zeros((0, 5))] * n

utils/utils.py

@@ -547,7 +547,7 @@ def plot_images(imgs, targets, fname='images.jpg'):
     plt.close()
 
 
-def plot_results(start=1, stop=0):  # from utils.utils import *; plot_results()
+def plot_results(start=0, stop=0):  # from utils.utils import *; plot_results()
     # Plot training results files 'results*.txt'
     # import os; os.system('wget https://storage.googleapis.com/ultralytics/yolov3/results_v3.txt')