diff --git a/tools/analysis_tools/analyze_logs.py b/tools/analysis_tools/analyze_logs.py
index 64b91c23c..fb6de805a 100644
--- a/tools/analysis_tools/analyze_logs.py
+++ b/tools/analysis_tools/analyze_logs.py
@@ -57,11 +57,9 @@ def plot_curve(log_dicts, args):
                     f'{args.json_logs[i]} does not contain metric {metric} '
                     f'in train mode')
 
-            if 'mAP' in metric:
-                xs = np.arange(1, max(epochs) + 1)
-                ys = []
-                for epoch in epochs:
-                    ys += log_dict[epoch][metric]
+            if any(m in metric for m in ('mAP', 'accuracy')):
+                xs = epochs
+                ys = [log_dict[e][metric] for e in xs]
                 ax = plt.gca()
                 ax.set_xticks(xs)
                 plt.xlabel('epoch')
@@ -74,6 +72,9 @@ def plot_curve(log_dicts, args):
                     iters = log_dict[epoch]['iter']
                     if log_dict[epoch]['mode'][-1] == 'val':
                         iters = iters[:-1]
+                    assert len(iters) > 0, (
+                        'The training log is empty, please try to reduce the '
+                        'interval of log in config file.')
                     xs.append(
                         np.array(iters) + (epoch - 1) * num_iters_per_epoch)
                     ys.append(np.array(log_dict[epoch][metric][:len(iters)]))