Replace 'ground truth' with 'labels' (#1337)

* Replace 'ground truth' with 'labels'

* Using torch... update

README.md

@@ -87,7 +87,7 @@ To run inference on example images in `data/images`:
 $ python detect.py --source data/images --weights yolov5s.pt --conf 0.25
 
 Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, output='runs/detect', save_conf=False, save_txt=False, source='data/images', update=False, view_img=False, weights='yolov5s.pt')
-Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16160MB)
+Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)
 
 Downloading https://github.com/ultralytics/yolov5/releases/download/v3.0/yolov5s.pt to yolov5s.pt... 100%|██████████████| 14.5M/14.5M [00:00<00:00, 21.3MB/s]
 

test.py

@@ -204,8 +204,8 @@ def test(data,
 
         # Plot images
         if plots and batch_i < 1:
-            f = save_dir / f'test_batch{batch_i}_gt.jpg'  # filename
-            plot_images(img, targets, paths, str(f), names)  # ground truth
+            f = save_dir / f'test_batch{batch_i}_labels.jpg'  # filename
+            plot_images(img, targets, paths, str(f), names)  # labels
             f = save_dir / f'test_batch{batch_i}_pred.jpg'
             plot_images(img, output_to_target(output, width, height), paths, str(f), names)  # predictions
 
@@ -250,9 +250,9 @@ def test(data,
             from pycocotools.cocoeval import COCOeval
 
             imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
-            cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0])  # initialize COCO ground truth api
-            cocoDt = cocoGt.loadRes(str(file))  # initialize COCO pred api
-            cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
+            cocoAnno = COCO(glob.glob('../coco/annotations/instances_val*.json')[0])  # initialize COCO annotations api
+            cocoPred = cocoAnno.loadRes(str(file))  # initialize COCO pred api
+            cocoEval = COCOeval(cocoAnno, cocoPred, 'bbox')
             cocoEval.params.imgIds = imgIds  # image IDs to evaluate
             cocoEval.evaluate()
             cocoEval.accumulate()

tutorial.ipynb

@@ -605,7 +605,7 @@
           "output_type": "stream",
           "text": [
             "Namespace(agnostic_nms=False, augment=False, classes=None, conf_thres=0.25, device='', img_size=640, iou_thres=0.45, save_conf=False, save_dir='runs/detect', save_txt=False, source='data/images/', update=False, view_img=False, weights=['yolov5s.pt'])\n",
-            "Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)\n",
+            "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
             "\n",
             "Fusing layers... \n",
             "Model Summary: 140 layers, 7.45958e+06 parameters, 0 gradients\n",
@@ -735,7 +735,7 @@
           "output_type": "stream",
           "text": [
             "Namespace(augment=False, batch_size=32, conf_thres=0.001, data='./data/coco.yaml', device='', img_size=640, iou_thres=0.65, save_conf=False, save_dir='runs/test', save_json=True, save_txt=False, single_cls=False, task='val', verbose=False, weights=['yolov5x.pt'])\n",
-            "Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)\n",
+            "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
             "\n",
             "Downloading https://github.com/ultralytics/yolov5/releases/download/v3.1/yolov5x.pt to yolov5x.pt...\n",
             "100% 170M/170M [00:05<00:00, 32.2MB/s]\n",
@@ -921,7 +921,7 @@
         {
           "output_type": "stream",
           "text": [
-            "Using CUDA device0 _CudaDeviceProperties(name='Tesla V100-SXM2-16GB', total_memory=16130MB)\n",
+            "Using torch 1.7.0+cu101 CUDA:0 (Tesla V100-SXM2-16GB, 16130MB)\n",
             "\n",
             "Namespace(adam=False, batch_size=16, bucket='', cache_images=True, cfg='', data='./data/coco128.yaml', device='', epochs=3, evolve=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], local_rank=-1, log_imgs=10, logdir='runs/', multi_scale=False, name='', noautoanchor=False, nosave=True, notest=False, rect=False, resume=False, single_cls=False, sync_bn=False, total_batch_size=16, weights='yolov5s.pt', workers=8, world_size=1)\n",
             "Start Tensorboard with \"tensorboard --logdir runs/\", view at http://localhost:6006/\n",
@@ -1040,7 +1040,7 @@
       },
       "source": [
         "Image(filename='runs/train/exp0/train_batch0.jpg', width=800)  # train batch 0 mosaics and labels\n",
-        "Image(filename='runs/train/exp0/test_batch0_gt.jpg', width=800)  # test batch 0 ground truth\n",
+        "Image(filename='runs/train/exp0/test_batch0_labels.jpg', width=800)  # test batch 0 labels\n",
         "Image(filename='runs/train/exp0/test_batch0_pred.jpg', width=800)  # test batch 0 predictions"
       ],
       "execution_count": null,
@@ -1056,7 +1056,7 @@
         "`train_batch0.jpg` train batch 0 mosaics and labels\n",
         "\n",
         "> <img src=\"https://user-images.githubusercontent.com/26833433/83667626-8c37fe00-a583-11ea-997b-0923fe59b29b.jpeg\" width=\"750\">  \n",
-        "`test_batch0_gt.jpg` shows test batch 0 ground truth\n",
+        "`test_batch0_labels.jpg` shows test batch 0 labels\n",
         "\n",
         "> <img src=\"https://user-images.githubusercontent.com/26833433/83667635-90641b80-a583-11ea-8075-606316cebb9c.jpeg\" width=\"750\">  \n",
         "`test_batch0_pred.jpg` shows test batch 0 _predictions_\n"

utils/general.py

@@ -275,10 +275,10 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-re
     ap, p, r = np.zeros(s), np.zeros(s), np.zeros(s)
     for ci, c in enumerate(unique_classes):
         i = pred_cls == c
-        n_gt = (target_cls == c).sum()  # Number of ground truth objects
-        n_p = i.sum()  # Number of predicted objects
+        n_l = (target_cls == c).sum()  # number of labels
+        n_p = i.sum()  # number of predictions
 
-        if n_p == 0 or n_gt == 0:
+        if n_p == 0 or n_l == 0:
             continue
         else:
             # Accumulate FPs and TPs
@@ -286,7 +286,7 @@ def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, fname='precision-re
             tpc = tp[i].cumsum(0)
 
             # Recall
-            recall = tpc / (n_gt + 1e-16)  # recall curve
+            recall = tpc / (n_l + 1e-16)  # recall curve
             r[ci] = np.interp(-pr_score, -conf[i], recall[:, 0])  # r at pr_score, negative x, xp because xp decreases
 
             # Precision
@@ -1076,8 +1076,8 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
             image_targets = targets[targets[:, 0] == i]
             boxes = xywh2xyxy(image_targets[:, 2:6]).T
             classes = image_targets[:, 1].astype('int')
-            gt = image_targets.shape[1] == 6  # ground truth if no conf column
-            conf = None if gt else image_targets[:, 6]  # check for confidence presence (gt vs pred)
+            labels = image_targets.shape[1] == 6  # labels if no conf column
+            conf = None if labels else image_targets[:, 6]  # check for confidence presence (label vs pred)
 
             boxes[[0, 2]] *= w
             boxes[[0, 2]] += block_x
@@ -1087,8 +1087,8 @@ def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max
                 cls = int(classes[j])
                 color = color_lut[cls % len(color_lut)]
                 cls = names[cls] if names else cls
-                if gt or conf[j] > 0.3:  # 0.3 conf thresh
-                    label = '%s' % cls if gt else '%s %.1f' % (cls, conf[j])
+                if labels or conf[j] > 0.3:  # 0.3 conf thresh
+                    label = '%s' % cls if labels else '%s %.1f' % (cls, conf[j])
                     plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)
 
         # Draw image filename labels