Single class train update (ultralytics#1719)

train.py

@@ -71,7 +71,8 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         check_dataset(data_dict)  # check
     train_path = data_dict['train']
     test_path = data_dict['val']
-    nc, names = (1, ['item']) if opt.single_cls else (int(data_dict['nc']), data_dict['names'])  # number classes, names
+    nc = 1 if opt.single_cls else int(data_dict['nc'])  # number of classes
+    names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names']  # class names
     assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data)  # check
 
     # Model
@@ -447,7 +448,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     parser.add_argument('--image-weights', action='store_true', help='use weighted image selection for training')
     parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
     parser.add_argument('--multi-scale', action='store_true', help='vary img-size +/- 50%%')
-    parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
+    parser.add_argument('--single-cls', action='store_true', help='train multi-class data as single-class')
     parser.add_argument('--adam', action='store_true', help='use torch.optim.Adam() optimizer')
     parser.add_argument('--sync-bn', action='store_true', help='use SyncBatchNorm, only available in DDP mode')
     parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')