diff --git a/main.py b/main.py
index 037e99c..4ec8333 100644
--- a/main.py
+++ b/main.py
@@ -146,10 +146,28 @@ def _executar_treino_sync(ambiente: str, pular_triagem: bool) -> dict:
                 if not k.endswith('.txt'):
                     redimensionar_imagem(caminho_local)
 
-    if len(os.listdir(img_dir)) > 0:
+    imagens = [f for f in os.listdir(img_dir) if not f.endswith('.txt')]
+    if len(imagens) > 0:
+        import random, shutil
+        random.shuffle(imagens)
+        corte = max(1, int(len(imagens) * 0.8))
+        val_imgs = imagens[corte:]
+
+        val_img_dir = f"{dataset_local}/val/images"
+        val_lbl_dir = f"{dataset_local}/val/labels"
+        os.makedirs(val_img_dir, exist_ok=True)
+        os.makedirs(val_lbl_dir, exist_ok=True)
+
+        for fname in val_imgs:
+            shutil.move(os.path.join(img_dir, fname), os.path.join(val_img_dir, fname))
+            lbl = fname.rsplit('.', 1)[0] + '.txt'
+            lbl_src = os.path.join(lbl_dir, lbl)
+            if os.path.exists(lbl_src):
+                shutil.move(lbl_src, os.path.join(val_lbl_dir, lbl))
+
         yaml_path = f"{dataset_local}/data.yaml"
         with open(yaml_path, 'w') as f:
-            yaml.dump({'train': img_dir, 'val': img_dir, 'nc': 1, 'names': {0: ambiente}}, f)
+            yaml.dump({'train': img_dir, 'val': val_img_dir, 'nc': 1, 'names': {0: ambiente}}, f)
 
         log_print(f"Treinando com {len(os.listdir(img_dir))} fotos ({'fine-tuning' if is_fine_tuning else 'do zero'})...")
         modelo_base.add_callback("on_fit_epoch_end", _on_fit_epoch_end)