From f06a9bbadd28c4567071408f9a27a103ba7cf265 Mon Sep 17 00:00:00 2001
From: fatih akyon <34196005+fcakyon@users.noreply.github.com>
Date: Fri, 10 Apr 2026 18:29:30 +0300
Subject: [PATCH] Fix train resume for non-end2end models (#24173)

Co-authored-by: Jing Qiu <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
---
 tests/test_engine.py          | 43 ++++++++++++++++++++++++++++++++++-
 ultralytics/engine/trainer.py |  2 +-
 2 files changed, 43 insertions(+), 2 deletions(-)

diff --git a/tests/test_engine.py b/tests/test_engine.py
index 1598ef51de..6440e49614 100644
--- a/tests/test_engine.py
+++ b/tests/test_engine.py
@@ -7,11 +7,12 @@ from unittest import mock
 import pytest
 import torch
 
-from tests import MODEL, SOURCE
+from tests import MODEL, SOURCE, TASK_MODEL_DATA
 from ultralytics import YOLO
 from ultralytics.cfg import get_cfg
 from ultralytics.engine.exporter import Exporter
 from ultralytics.models.yolo import classify, detect, segment
+from ultralytics.nn.tasks import load_checkpoint
 from ultralytics.utils import ASSETS, DEFAULT_CFG, WEIGHTS_DIR
 
 
@@ -127,6 +128,46 @@ def test_classify():
     result = pred(source=ASSETS, model=trainer.best)
     assert len(result), "predictor test failed"
 
+    # Test resume functionality
+    with pytest.raises(AssertionError):
+        classify.ClassificationTrainer(overrides={**overrides, "resume": trainer.last}).train()
+
+
+@pytest.mark.parametrize("task,weight,data", TASK_MODEL_DATA)
+def test_resume_incomplete(task, weight, data, tmp_path):
+    """Test training resumes from an incomplete checkpoint."""
+    train_args = {
+        "data": data,
+        "epochs": 2,
+        "save": True,
+        "plots": False,
+        "workers": 0,
+        "project": tmp_path,
+        "name": task,
+        "imgsz": 32,
+        "exist_ok": True,
+    }
+
+    def stop_after_first_epoch(trainer):
+        if trainer.epoch == 0:
+            trainer.stop = True
+
+    def disable_final_eval(trainer):
+        trainer.final_eval = lambda: None
+
+    model = YOLO(weight)
+    model.add_callback("on_train_start", disable_final_eval)
+    model.add_callback("on_train_epoch_end", stop_after_first_epoch)
+    model.train(**train_args)
+    last_path = model.trainer.last
+    _, ckpt = load_checkpoint(last_path)
+    assert ckpt["epoch"] == 0, "checkpoint should be resumable"
+
+    # Resume training using the checkpoint
+    resume_model = YOLO(last_path)
+    resume_model.train(resume=True, **train_args)
+    assert resume_model.trainer.start_epoch == resume_model.trainer.epoch == 1, "resume test failed"
+
 
 def test_nan_recovery():
     """Test NaN loss detection and recovery during training."""
diff --git a/ultralytics/engine/trainer.py b/ultralytics/engine/trainer.py
index 4484f2e251..49b9d937ac 100644
--- a/ultralytics/engine/trainer.py
+++ b/ultralytics/engine/trainer.py
@@ -961,7 +961,7 @@ class BaseTrainer:
             )
             self.epochs += ckpt["epoch"]  # finetune additional epochs
         self._load_checkpoint_state(ckpt)
-        if unwrap_model(self.model).end2end:
+        if getattr(unwrap_model(self.model), "end2end", False):
             # initialize loss and resume o2o and o2m args
             unwrap_model(self.model).criterion = unwrap_model(self.model).init_criterion()
             unwrap_model(self.model).criterion.updates = start_epoch - 1