Addressing the comments.

tools/train_net.py

@@ -46,7 +46,7 @@ def get_augs(cfg):
     augs.append(T.RandomFlip(horizontal=horizontal_flip,
                              vertical=not horizontal_flip))
     # Rotate the image between -90 to 0 degrees clockwise around the centre
-    augs.append(T.RandomRotation(angle=[90.0, 0.0]))
+    augs.append(T.RandomRotation(angle=[-90.0, 0.0]))
     return augs
 
 class Trainer(DefaultTrainer):
@@ -171,11 +171,11 @@ if __name__ == "__main__":
     parser = default_argument_parser()
 
     # Extra Configurations for dataset names and paths
-    parser.add_argument("--dataset_name", default="", help="The Dataset Name")
+    parser.add_argument("--dataset_name",          default="", help="The Dataset Name")
     parser.add_argument("--json_annotation_train", default="", metavar="FILE", help="The path to the training set JSON annotation")
-    parser.add_argument("--image_path_train", default="", metavar="FILE", help="The path to the training set image folder")
-    parser.add_argument("--json_annotation_val", default="", metavar="FILE", help="The path to the validation set JSON annotation")
-    parser.add_argument("--image_path_val", default="", metavar="FILE", help="The path to the validation set image folder")
+    parser.add_argument("--image_path_train",      default="", metavar="FILE", help="The path to the training set image folder")
+    parser.add_argument("--json_annotation_val",   default="", metavar="FILE", help="The path to the validation set JSON annotation")
+    parser.add_argument("--image_path_val",        default="", metavar="FILE", help="The path to the validation set image folder")
 
     args = parser.parse_args()
     print("Command Line Args:", args)

utils/cocosplit.py

@@ -10,7 +10,7 @@ parser.add_argument('--annotation_path', metavar='coco_annotations', type=str,
                     help='Path to COCO annotations file.')
 parser.add_argument('--train', type=str, help='Where to store COCO training annotations')
 parser.add_argument('--test', type=str, help='Where to store COCO test annotations')
-parser.add_argument('--s', dest='split_ratio', type=float, required=True,
+parser.add_argument('--split-ratio', dest='split_ratio', type=float, required=True,
                     help="A percentage of a split; a number in (0, 1)")
 parser.add_argument('--having-annotations', dest='having_annotations', action='store_true',
                     help='Ignore all images without annotations. Keep only these with at least one annotation')
@@ -33,47 +33,45 @@ def main(annotation_path,
     with open(annotation_path, 'rt', encoding='UTF-8') as annotations:
         coco = json.load(annotations)
 
-        images = coco['images']
-        annotations = coco['annotations']
+    images = coco['images']
+    annotations = coco['annotations']
 
-        number_of_images = len(images)
+    ids_with_annotations = funcy.lmap(lambda a: int(a['image_id']), annotations)
 
-        ids_with_annotations = funcy.lmap(lambda a: int(a['image_id']), annotations)
+    # Images with annotations
+    img_ann = funcy.lremove(lambda i: i['id'] not in ids_with_annotations, images)
+    tr_ann, ts_ann = train_test_split(img_ann, train_size=split_ratio,
+                                      random_state=random_state)
 
-        # Images with annotations
-        img_ann = funcy.lremove(lambda i: i['id'] not in ids_with_annotations, images)
-        tr_ann, ts_ann = train_test_split(img_ann, train_size=split_ratio,
-                                          random_state=random_state)
+    # Images without annotations
+    img_wo_ann = funcy.lremove(lambda i: i['id'] in ids_with_annotations, images)
+    tr_wo_ann, ts_wo_ann = train_test_split(img_wo_ann, train_size=split_ratio,
+                                            random_state=random_state)
 
-        # Images without annotations
-        img_wo_ann = funcy.lremove(lambda i: i['id'] in ids_with_annotations, images)
-        tr_wo_ann, ts_wo_ann = train_test_split(img_wo_ann, train_size=split_ratio,
-                                                random_state=random_state)
+    if having_annotations:
+        tr, ts = tr_ann, ts_ann
 
-        if having_annotations:
-            tr, ts = tr_ann, ts_ann
+    else:
+        # Merging the 2 image lists (i.e. with and without annotation)
+        tr_ann.extend(tr_wo_ann)
+        ts_ann.extend(ts_wo_ann)
 
-        else:
-            # Merging the 2 image lists (i.e. with and without annotation)
-            tr_ann.extend(tr_wo_ann)
-            ts_ann.extend(ts_wo_ann)
+        tr, ts = tr_ann, ts_ann
 
-            tr, ts = tr_ann, ts_ann
+    # Train Data
+    coco.update({'images': tr,
+                 'annotations': filter_annotations(annotations, tr)})
+    save_coco(train_save_path, coco)
 
-        # Train Data
-        coco.update({'images': tr,
-                     'annotations': filter_annotations(annotations, tr)})
-        save_coco(train_save_path, coco)
+    # Test Data
+    coco.update({'images': ts,
+                 'annotations': filter_annotations(annotations, ts)})
+    save_coco(test_save_path, coco)
 
-        # Test Data
-        coco.update({'images': ts,
-                     'annotations': filter_annotations(annotations, ts)})
-        save_coco(test_save_path, coco)
-
-        print("Saved {} entries in {} and {} in {}".format(len(tr),
-                                                           train_save_path,
-                                                           len(ts),
-                                                           test_save_path))
+    print("Saved {} entries in {} and {} in {}".format(len(tr),
+                                                       train_save_path,
+                                                       len(ts),
+                                                       test_save_path))
 
 
 if __name__ == "__main__":