diff --git a/train.py b/train.py
index 9efece250581..390ff86a2593 100644
--- a/train.py
+++ b/train.py
@@ -24,7 +24,7 @@
 from copy import deepcopy
 from datetime import datetime
 from pathlib import Path
-
+from pprint import pformat, pprint
 import numpy as np
 import torch
 import torch.distributed as dist
@@ -65,6 +65,16 @@
 WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
 
 
+def print_dict(d):
+    for key, value in d.items():
+        LOGGER.info(f"{key}: {value}")
+
+
+def print_namespace(ns):
+    for attr in vars(ns):
+        LOGGER.info(f"{attr}: {getattr(ns, attr)}")
+
+
 def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictionary
     save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
         Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
@@ -384,6 +394,10 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
                 if best_fitness == fi:
                     torch.save(ckpt, best)
                 if opt.save_period > 0 and epoch % opt.save_period == 0:
+                    LOGGER.info(f'Saving checkpoint at {epoch} epochs')
+                    LOGGER.info('optの中身')
+                    print_namespace(opt)
+                    print_dict(ckpt)
                     torch.save(ckpt, w / f'epoch{epoch}.pt')
                 del ckpt
                 callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)