diff --git a/detect.py b/detect.py
index 56194118..2f3fb940 100644
--- a/detect.py
+++ b/detect.py
@@ -2,7 +2,7 @@ import argparse
 import time
 from sys import platform
 
-from models import *
+from models import *  # set ONNX_EXPORT in models.py
 from utils.datasets import *
 from utils.utils import *
 
@@ -14,7 +14,6 @@ def detect(save_txt=False, save_img=True, stream_img=False):
 
     # Initialize
     device = torch_utils.select_device(force_cpu=ONNX_EXPORT)
-    torch.backends.cudnn.benchmark = False  # set False to speed up variable image size inference
     if os.path.exists(out):
         shutil.rmtree(out)  # delete output folder
     os.makedirs(out)  # make new output folder
@@ -30,6 +29,7 @@ def detect(save_txt=False, save_img=True, stream_img=False):
 
     # Fuse Conv2d + BatchNorm2d layers
     # model.fuse()
+    # torch.backends.cudnn.benchmark = True  # set True to speed up constant image size inference
 
     # Eval mode
     model.to(device).eval()
@@ -50,9 +50,9 @@ def detect(save_txt=False, save_img=True, stream_img=False):
     if webcam:
         save_img = False
         stream_img = True
-        dataloader = LoadWebcam(opt.source, img_size=img_size, half=opt.half)
+        dataset = LoadWebcam(opt.source, img_size=img_size, half=opt.half)
     else:
-        dataloader = LoadImages(opt.source, img_size=img_size, half=opt.half)
+        dataset = LoadImages(opt.source, img_size=img_size, half=opt.half)
 
     # Get classes and colors
     classes = load_classes(parse_data_cfg(opt.data)['names'])
@@ -60,7 +60,7 @@ def detect(save_txt=False, save_img=True, stream_img=False):
 
     # Run inference
     t0 = time.time()
-    for path, img, im0, vid_cap in dataloader:
+    for path, img, im0, vid_cap in dataset:
         t = time.time()
         save_path = str(Path(out) / Path(path).name)
 
@@ -69,15 +69,15 @@ def detect(save_txt=False, save_img=True, stream_img=False):
         pred, _ = model(img)
         det = non_max_suppression(pred.float(), opt.conf_thres, opt.nms_thres)[0]
 
+        s = '%gx%g ' % img.shape[2:]  # string to print image size
         if det is not None and len(det):
             # Rescale boxes from img_size to im0 size
             det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
 
-            # Print results to screen
-            print('%gx%g ' % img.shape[2:], end='')  # print image size
+            # Print results
             for c in det[:, -1].unique():
-                n = (det[:, -1] == c).sum()
-                print('%g %ss' % (n, classes[int(c)]), end=', ')
+                n = (det[:, -1] == c).sum()  # detections per class
+                s += '%g %ss' % (n, classes[int(c)])  # add to string
 
             # Write results
             for *xyxy, conf, _, cls in det:
@@ -89,13 +89,15 @@ def detect(save_txt=False, save_img=True, stream_img=False):
                     label = '%s %.2f' % (classes[int(cls)], conf)
                     plot_one_box(xyxy, im0, label=label, color=colors[int(cls)])
 
-        print('Done. (%.3fs)' % (time.time() - t))
+        print('%sDone. (%.3fs)' % (s, time.time() - t))
 
-        if stream_img:  # Stream results
+        # Stream results
+        if stream_img:
             cv2.imshow(opt.weights, im0)
 
-        if save_img:  # Save image with detections
-            if dataloader.mode == 'images':
+        # Save results (image with detections)
+        if save_img:
+            if dataset.mode == 'images':
                 cv2.imwrite(save_path, im0)
             else:
                 if vid_path != save_path:  # new video