diff --git a/training/training_loop.py b/training/training_loop.py
index 14836ad2e..f83f04a6e 100755
--- a/training/training_loop.py
+++ b/training/training_loop.py
@@ -7,6 +7,7 @@
 # license agreement from NVIDIA CORPORATION is strictly prohibited.
 
 import os
+import re
 import time
 import copy
 import json
@@ -152,6 +153,7 @@ def training_loop(
     G_ema = copy.deepcopy(G).eval()
 
     # Resume from existing pickle.
+    cur_nimg = 0
     if (resume_pkl is not None) and (rank == 0):
         print(f'Resuming from "{resume_pkl}"')
         with dnnlib.util.open_url(resume_pkl) as f:
@@ -159,6 +161,12 @@ def training_loop(
         for name, module in [('G', G), ('D', D), ('G_ema', G_ema)]:
             misc.copy_params_and_buffers(resume_data[name], module, require_all=False)
 
+        # resume from a snapshot file(`network-snapshot-<INT>.pkl`) continues from
+        # where it stopped..
+        match = re.match(r"^.*(network-snapshot-)(\d+)(.pkl)$", resume_pkl, re.IGNORECASE)
+        if match:
+            cur_nimg = int(match.group(2)) * 1000
+
     # Print network summary tables.
     if rank == 0:
         z = torch.empty([batch_gpu, G.z_dim], device=device)
@@ -245,14 +253,14 @@ def training_loop(
     if rank == 0:
         print(f'Training for {total_kimg} kimg...')
         print()
-    cur_nimg = 0
+
     cur_tick = 0
     tick_start_nimg = cur_nimg
     tick_start_time = time.time()
     maintenance_time = tick_start_time - start_time
     batch_idx = 0
     if progress_fn is not None:
-        progress_fn(0, total_kimg)
+        progress_fn(cur_nimg // 1000, total_kimg)
     while True:
 
         # Fetch training data.