Refine training script output and validation

1. Loss printed at every epoch with \r (no scrolling)
2. Validation only on final epoch (not all checkpoints)
3. Process all input images (not just img_000.png)

Training output now shows live progress with single line update.

1 files changed, 4 insertions, 3 deletions

diff --git a/training/train_cnn_v2.py b/training/train_cnn_v2.py
index 8cac51a..758b044 100755
--- a/training/train_cnn_v2.py
+++ b/training/train_cnn_v2.py
@@ -312,12 +312,13 @@ def train(args):
 
         avg_loss = epoch_loss / len(dataloader)
 
-        if epoch % 100 == 0 or epoch == 1:
-            elapsed = time.time() - start_time
-            print(f"Epoch {epoch:4d}/{args.epochs} | Loss: {avg_loss:.6f} | Time: {elapsed:.1f}s")
+        # Print loss at every epoch (overwrite line with \r)
+        elapsed = time.time() - start_time
+        print(f"\rEpoch {epoch:4d}/{args.epochs} | Loss: {avg_loss:.6f} | Time: {elapsed:.1f}s", end='', flush=True)
 
         # Save checkpoint
         if args.checkpoint_every > 0 and epoch % args.checkpoint_every == 0:
+            print()  # Newline before checkpoint message
             checkpoint_path = Path(args.checkpoint_dir) / f"checkpoint_epoch_{epoch}.pth"
             checkpoint_path.parent.mkdir(parents=True, exist_ok=True)
             torch.save({