Raise helpful warnings with Faster R-CNN train script

examples/detection/eval_voc07.py

@@ -48,7 +48,7 @@ def main():
                 n_fg_class=20,
                 pretrained_model=args.pretrained_model)
         else:
-            model = FasterRCNNVGG16(pretrained_model='voc0712')
+            model = FasterRCNNVGG16(pretrained_model='voc07')
     elif args.model == 'ssd300':
         if args.pretrained_model:
             model = SSD300(

examples/detection/visualize_models.py

@@ -13,7 +13,7 @@
 def main():
     dataset = VOCDetectionDataset(year='2007', split='test')
     models = [
-        ('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc0712')),
+        ('Faster R-CNN', FasterRCNNVGG16(pretrained_model='voc07')),
         ('SSD300', SSD300(pretrained_model='voc0712')),
         ('SSD512', SSD512(pretrained_model='voc0712')),
     ]

examples/faster_rcnn/README.md

@@ -45,7 +45,7 @@ On top of that, the anchors are not discretized in ChainerCV.
 
 
 ### Train code
-For training with VOC2007
+For training with VOC2007 (this setting is used by default)
 ```
 $ python train.py --dataset voc07 --step_size 50000 --iteration 70000 [--gpu <gpu>]
 ```

examples/faster_rcnn/demo.py

@@ -12,7 +12,7 @@
 def main():
     parser = argparse.ArgumentParser()
     parser.add_argument('--gpu', type=int, default=-1)
-    parser.add_argument('--pretrained_model', default='voc0712')
+    parser.add_argument('--pretrained_model', default='voc07')
     parser.add_argument('image')
     args = parser.parse_args()
 

examples/faster_rcnn/train.py

@@ -61,14 +61,15 @@ def main():
     parser = argparse.ArgumentParser(
         description='ChainerCV training example: Faster R-CNN')
     parser.add_argument('--dataset', choices=('voc07', 'voc0712'),
-                        help='The dataset to use: VOC07, VOC07+12')
+                        help='The dataset to use: VOC07, VOC07+12',
+                        default='voc07')
     parser.add_argument('--gpu', '-g', type=int, default=-1)
     parser.add_argument('--lr', '-l', type=float, default=1e-3)
     parser.add_argument('--out', '-o', default='result',
                         help='Output directory')
     parser.add_argument('--seed', '-s', type=int, default=0)
-    parser.add_argument('--step_size', '-ss', type=int)
-    parser.add_argument('--iteration', '-i', type=int)
+    parser.add_argument('--step_size', '-ss', type=int, default=50000)
+    parser.add_argument('--iteration', '-i', type=int, default=70000)
     args = parser.parse_args()
 
     np.random.seed(args.seed)