--- a
+++ b/tools/data/activitynet/process_annotations.py
@@ -0,0 +1,54 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""This file processes the annotation files and generates proper annotation
+files for localizers."""
+import json
+
+import numpy as np
+
+
+def load_json(file):
+    with open(file) as json_file:
+        data = json.load(json_file)
+        return data
+
+
+data_file = '../../../data/ActivityNet'
+info_file = f'{data_file}/video_info_new.csv'
+ann_file = f'{data_file}/anet_anno_action.json'
+
+anno_database = load_json(ann_file)
+
+video_record = np.loadtxt(info_file, dtype=np.str, delimiter=',', skiprows=1)
+
+video_dict_train = {}
+video_dict_val = {}
+video_dict_test = {}
+video_dict_full = {}
+
+for _, video_item in enumerate(video_record):
+    video_name = video_item[0]
+    video_info = anno_database[video_name]
+    video_subset = video_item[5]
+    video_info['fps'] = video_item[3].astype(np.float)
+    video_info['rfps'] = video_item[4].astype(np.float)
+    video_dict_full[video_name] = video_info
+    if video_subset == 'training':
+        video_dict_train[video_name] = video_info
+    elif video_subset == 'testing':
+        video_dict_test[video_name] = video_info
+    elif video_subset == 'validation':
+        video_dict_val[video_name] = video_info
+
+print(f'full subset video numbers: {len(video_record)}')
+
+with open(f'{data_file}/anet_anno_train.json', 'w') as result_file:
+    json.dump(video_dict_train, result_file)
+
+with open(f'{data_file}/anet_anno_val.json', 'w') as result_file:
+    json.dump(video_dict_val, result_file)
+
+with open(f'{data_file}/anet_anno_test.json', 'w') as result_file:
+    json.dump(video_dict_test, result_file)
+
+with open(f'{data_file}/anet_anno_full.json', 'w') as result_file:
+    json.dump(video_dict_full, result_file)