--- a
+++ b/tools/analysis/report_map.py
@@ -0,0 +1,87 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import os
+import os.path as osp
+
+import mmcv
+import numpy as np
+
+from mmaction.core import ActivityNetLocalization
+
+args = None
+
+
+def cuhk17_top1():
+    """Assign label for each proposal with the cuhk17 result, which is the #2
+    entry in http://activity-net.org/challenges/2017/evaluation.html."""
+    if not osp.exists('cuhk_anet17_pred.json'):
+        os.system('wget https://download.openmmlab.com/'
+                  'mmaction/localization/cuhk_anet17_pred.json')
+    proposal = mmcv.load(args.proposal)
+    results = proposal['results']
+    cuhk_pred = mmcv.load('cuhk_anet17_pred.json')['results']
+
+    def get_topk(preds, k):
+        preds.sort(key=lambda x: x['score'])
+        return preds[-k:]
+
+    for k, v in results.items():
+        action_pred = cuhk_pred[k]
+        top1 = get_topk(action_pred, 1)
+        top1_label = top1[0]['label']
+        new_value = []
+        for item in v:
+            x = dict(label=top1_label)
+            x.update(item)
+            new_value.append(x)
+        results[k] = new_value
+    proposal['results'] = results
+    mmcv.dump(proposal, args.det_output)
+
+
+cls_funcs = {'cuhk17_top1': cuhk17_top1}
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Report detection mAP for'
+                                     'ActivityNet proposal file')
+    parser.add_argument('--proposal', type=str, help='proposal file')
+    parser.add_argument(
+        '--gt',
+        type=str,
+        default='data/ActivityNet/'
+        'anet_anno_val.json',
+        help='groundtruth file')
+    parser.add_argument(
+        '--cls',
+        type=str,
+        default='cuhk17_top1',
+        choices=['cuhk17_top1'],
+        help='the way to assign label for each '
+        'proposal')
+    parser.add_argument(
+        '--det-output',
+        type=str,
+        default='det_result.json',
+        help='the path to store detection results')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    global args, cls_funcs
+    args = parse_args()
+    func = cls_funcs[args.cls]
+    func()
+    anet_detection = ActivityNetLocalization(
+        args.gt,
+        args.det_output,
+        tiou_thresholds=np.linspace(0.5, 0.95, 10),
+        verbose=True)
+    mAP, average_mAP = anet_detection.evaluate()
+    print('[RESULTS] Performance on ActivityNet detection task.\n'
+          f'mAP: {mAP}\nAverage-mAP: {average_mAP}')
+
+
+if __name__ == '__main__':
+    main()