diff --git a/distillation/config/config_stdc.yaml b/distillation/config/config_stdc.yaml
index 5f6da79..75bb24b 100644
--- a/distillation/config/config_stdc.yaml
+++ b/distillation/config/config_stdc.yaml
@@ -78,4 +78,6 @@ checkpoints:
   filename: '{epoch}-{val_similarity:.2f}'
   monitor: val_similarity
   mode: max
-  save_top_k: 3
\ No newline at end of file
+  save_top_k: 3
+
+resume_from_checkpoint: /home/arda/dinov2/distillation/checkpoints/stdc/epoch=2-val_similarity=0.33.ckpt
\ No newline at end of file
diff --git a/distillation/train.py b/distillation/train.py
index f9f6c9d..662abc8 100644
--- a/distillation/train.py
+++ b/distillation/train.py
@@ -106,28 +106,51 @@ class DistillationTrainer:
 
         logger = TensorBoardLogger(f"logs/{self.cfg.train.name}", name="distillation")
         logger.log_hyperparams(self.cfg)
-    
+        checkpoint_dir = os.path.join(self.cfg.checkpoints.dirpath, self.cfg.train.name)
+        checkpoint_path = self._find_latest_checkpoint(checkpoint_dir)
+
         # Also save config as text for better readability
         experiment_dir = logger.log_dir
         os.makedirs(experiment_dir, exist_ok=True)
         config_path = os.path.join(experiment_dir, 'config.yaml')
         OmegaConf.save(self.cfg, config_path)
 
+        if checkpoint_path:
+            print(f"Resuming training from checkpoint: {checkpoint_path}")
+        else:
+            print("Starting training from scratch")
+
         return pl.Trainer(
             max_epochs=self.training_config.max_epochs,
             accelerator=self.cfg.train.accelerator,
             devices=self.cfg.train.devices,
             num_nodes=self.cfg.train.num_nodes,
-            strategy=self.cfg.train.strategy,  # 'ddp', 'deepspeed', etc.
+            strategy=self.cfg.train.strategy,
             precision=self.training_config.precision,
             callbacks=[checkpoint_callback],
             logger=logger,
+            resume_from_checkpoint=checkpoint_path
         )
 
     def train(self):
         """Execute training pipeline."""
         self.trainer.fit(self.distillation_module, self.data_module)
 
+    def _find_latest_checkpoint(self, checkpoint_dir: str) -> str | None:
+        """Find the latest checkpoint in the specified directory."""
+        if not os.path.exists(checkpoint_dir):
+            return None
+            
+        checkpoints = [f for f in os.listdir(checkpoint_dir) if f.endswith('.ckpt')]
+        if not checkpoints:
+            return None
+            
+        # Sort by modification time, newest first
+        latest_checkpoint = max(
+            checkpoints,
+            key=lambda x: os.path.getmtime(os.path.join(checkpoint_dir, x))
+        )
+        return os.path.join(checkpoint_dir, latest_checkpoint)
 
 def setup_environment():
     """Setup environment configurations."""