Updated Coco Example

samples/coco/coco.py

@@ -479,12 +479,14 @@ if __name__ == '__main__':
         # validation set, as as in the Mask RCNN paper.
         dataset_train = CocoDataset()
         dataset_train.load_coco(args.dataset, "train", year=args.year, auto_download=args.download)
-        dataset_train.load_coco(args.dataset, "valminusminival", year=args.year, auto_download=args.download)
+        if args.year in '2014':
+            dataset_train.load_coco(args.dataset, "valminusminival", year=args.year, auto_download=args.download)
         dataset_train.prepare()
 
         # Validation dataset
         dataset_val = CocoDataset()
-        dataset_val.load_coco(args.dataset, "minival", year=args.year, auto_download=args.download)
+        val_type = "val" if args.year in '2017' else "minival"
+        dataset_val.load_coco(args.dataset, val_type, year=args.year, auto_download=args.download)
         dataset_val.prepare()
 
         # Image Augmentation
@@ -522,7 +524,8 @@ if __name__ == '__main__':
     elif args.command == "evaluate":
         # Validation dataset
         dataset_val = CocoDataset()
-        coco = dataset_val.load_coco(args.dataset, "minival", year=args.year, return_coco=True, auto_download=args.download)
+        val_type = "val" if args.year in '2017' else "minival"
+        coco = dataset_val.load_coco(args.dataset, val_type, year=args.year, return_coco=True, auto_download=args.download)
         dataset_val.prepare()
         print("Running COCO evaluation on {} images.".format(args.limit))
         evaluate_coco(model, dataset_val, coco, "bbox", limit=int(args.limit))