Refactor constants management to use Pydantic BaseModel for configuration

- Replaced module-level path variables in constants.py with a structured Pydantic Config class.
- Updated all relevant modules (train.py, augmentation.py, exports.py, dataset-visualiser.py, manual_run.py) to access paths through the new config structure.
- Fixed bugs related to image processing and model saving.
- Enhanced test infrastructure to accommodate the new configuration approach.

This refactor improves code maintainability and clarity by centralizing configuration management.

tests/conftest.py

@@ -8,35 +8,14 @@ _DATASET_IMAGES = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/images"
 _DATASET_LABELS = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/labels"
 _ONNX_MODEL = _PROJECT_ROOT / "_docs/00_problem/input_data/azaion.onnx"
 _CLASSES_JSON = _PROJECT_ROOT / "classes.json"
+_CONFIG_TEST = _PROJECT_ROOT / "config.test.yaml"
 
 collect_ignore = ["security_test.py", "imagelabel_visualize_test.py"]
 
 
 def apply_constants_patch(monkeypatch, base: Path):
     import constants as c
-    from os import path
-
-    root = str(base.resolve())
-    azaion = path.join(root, "azaion")
-    monkeypatch.setattr(c, "azaion", azaion)
-    data_dir = path.join(azaion, "data")
-    monkeypatch.setattr(c, "data_dir", data_dir)
-    monkeypatch.setattr(c, "data_images_dir", path.join(data_dir, c.images))
-    monkeypatch.setattr(c, "data_labels_dir", path.join(data_dir, c.labels))
-    processed_dir = path.join(azaion, "data-processed")
-    monkeypatch.setattr(c, "processed_dir", processed_dir)
-    monkeypatch.setattr(c, "processed_images_dir", path.join(processed_dir, c.images))
-    monkeypatch.setattr(c, "processed_labels_dir", path.join(processed_dir, c.labels))
-    corrupted_dir = path.join(azaion, "data-corrupted")
-    monkeypatch.setattr(c, "corrupted_dir", corrupted_dir)
-    monkeypatch.setattr(c, "corrupted_images_dir", path.join(corrupted_dir, c.images))
-    monkeypatch.setattr(c, "corrupted_labels_dir", path.join(corrupted_dir, c.labels))
-    monkeypatch.setattr(c, "sample_dir", path.join(azaion, "data-sample"))
-    monkeypatch.setattr(c, "datasets_dir", path.join(azaion, "datasets"))
-    models_dir = path.join(azaion, "models")
-    monkeypatch.setattr(c, "models_dir", models_dir)
-    monkeypatch.setattr(c, "CURRENT_PT_MODEL", path.join(models_dir, f"{c.prefix[:-1]}.pt"))
-    monkeypatch.setattr(c, "CURRENT_ONNX_MODEL", path.join(models_dir, f"{c.prefix[:-1]}.onnx"))
+    monkeypatch.setattr(c, "config", c.Config.from_yaml(str(_CONFIG_TEST), root=str(base / "azaion")))
 
 
 @pytest.fixture(scope="session")

tests/performance/test_augmentation_perf.py

@@ -20,15 +20,7 @@ if "matplotlib" not in sys.modules:
 
 
 def _patch_augmentation_paths(monkeypatch, base: Path):
-    import augmentation as aug
-    import constants as c
-
     apply_constants_patch(monkeypatch, base)
-    monkeypatch.setattr(aug, "data_images_dir", c.data_images_dir)
-    monkeypatch.setattr(aug, "data_labels_dir", c.data_labels_dir)
-    monkeypatch.setattr(aug, "processed_images_dir", c.processed_images_dir)
-    monkeypatch.setattr(aug, "processed_labels_dir", c.processed_labels_dir)
-    monkeypatch.setattr(aug, "processed_dir", c.processed_dir)
 
 
 def _augment_annotation_with_total(monkeypatch):
@@ -58,8 +50,8 @@ def test_pt_aug_01_throughput_ten_images_sixty_seconds(
     import constants as c
     from augmentation import Augmentator
 
-    img_dir = Path(c.data_images_dir)
-    lbl_dir = Path(c.data_labels_dir)
+    img_dir = Path(c.config.data_images_dir)
+    lbl_dir = Path(c.config.data_labels_dir)
     img_dir.mkdir(parents=True, exist_ok=True)
     lbl_dir.mkdir(parents=True, exist_ok=True)
     src_img, src_lbl = sample_images_labels(10)
@@ -83,9 +75,9 @@ def test_pt_aug_02_parallel_at_least_one_point_five_x_faster(
     import constants as c
     from augmentation import Augmentator
 
-    img_dir = Path(c.data_images_dir)
-    lbl_dir = Path(c.data_labels_dir)
-    proc_dir = Path(c.processed_dir)
+    img_dir = Path(c.config.data_images_dir)
+    lbl_dir = Path(c.config.data_labels_dir)
+    proc_dir = Path(c.config.processed_dir)
     img_dir.mkdir(parents=True, exist_ok=True)
     lbl_dir.mkdir(parents=True, exist_ok=True)
     src_img, src_lbl = sample_images_labels(10)
@@ -93,8 +85,8 @@ def test_pt_aug_02_parallel_at_least_one_point_five_x_faster(
         shutil.copy2(p, img_dir / p.name)
     for p in src_lbl.glob("*.txt"):
         shutil.copy2(p, lbl_dir / p.name)
-    Path(c.processed_images_dir).mkdir(parents=True, exist_ok=True)
-    Path(c.processed_labels_dir).mkdir(parents=True, exist_ok=True)
+    Path(c.config.processed_images_dir).mkdir(parents=True, exist_ok=True)
+    Path(c.config.processed_labels_dir).mkdir(parents=True, exist_ok=True)
     names = sorted(p.name for p in img_dir.glob("*.jpg"))
 
     class _E:
@@ -113,8 +105,8 @@ def test_pt_aug_02_parallel_at_least_one_point_five_x_faster(
     seq_elapsed = time.perf_counter() - t0
 
     shutil.rmtree(proc_dir)
-    Path(c.processed_images_dir).mkdir(parents=True, exist_ok=True)
-    Path(c.processed_labels_dir).mkdir(parents=True, exist_ok=True)
+    Path(c.config.processed_images_dir).mkdir(parents=True, exist_ok=True)
+    Path(c.config.processed_labels_dir).mkdir(parents=True, exist_ok=True)
 
     aug_par = Augmentator()
     aug_par.total_images_to_process = len(entries)

tests/performance/test_dataset_perf.py

@@ -56,8 +56,8 @@ def _prepare_form_dataset(
     constants_patch(tmp_path)
     import train
 
-    proc_img = Path(c_mod.processed_images_dir)
-    proc_lbl = Path(c_mod.processed_labels_dir)
+    proc_img = Path(c_mod.config.processed_images_dir)
+    proc_lbl = Path(c_mod.config.processed_labels_dir)
     proc_img.mkdir(parents=True, exist_ok=True)
     proc_lbl.mkdir(parents=True, exist_ok=True)
 
@@ -70,14 +70,8 @@ def _prepare_form_dataset(
         if stem in corrupt_stems:
             dst.write_text("0 1.5 0.5 0.1 0.1\n", encoding="utf-8")
 
-    today_ds = osp.join(c_mod.datasets_dir, train.today_folder)
-    monkeypatch.setattr(train, "today_dataset", today_ds)
-    monkeypatch.setattr(train, "processed_images_dir", c_mod.processed_images_dir)
-    monkeypatch.setattr(train, "processed_labels_dir", c_mod.processed_labels_dir)
-    monkeypatch.setattr(train, "corrupted_images_dir", c_mod.corrupted_images_dir)
-    monkeypatch.setattr(train, "corrupted_labels_dir", c_mod.corrupted_labels_dir)
-    monkeypatch.setattr(train, "datasets_dir", c_mod.datasets_dir)
-    return train
+    today_ds = osp.join(c_mod.config.datasets_dir, train.today_folder)
+    return train, today_ds
 
 
 @pytest.mark.performance
@@ -88,7 +82,7 @@ def test_pt_dsf_01_dataset_formation_under_thirty_seconds(
     fixture_images_dir,
     fixture_labels_dir,
 ):
-    train = _prepare_form_dataset(
+    train, today_ds = _prepare_form_dataset(
         monkeypatch,
         tmp_path,
         constants_patch,

tests/test_annotation_classes.py

@@ -42,9 +42,13 @@ def data_yaml_text(monkeypatch, tmp_path, fixture_classes_json):
     _stub_train_imports()
     import train
 
-    monkeypatch.setattr(train, "today_dataset", str(tmp_path))
+    import constants as c
+    monkeypatch.setattr(c, "config", c.Config(dirs=c.DirsConfig(root=str(tmp_path))))
+    monkeypatch.setattr(train, "today_folder", "")
+    from pathlib import Path
+    Path(c.config.datasets_dir).mkdir(parents=True, exist_ok=True)
     train.create_yaml()
-    return (tmp_path / "data.yaml").read_text(encoding="utf-8")
+    return (Path(c.config.datasets_dir) / "data.yaml").read_text(encoding="utf-8")
 
 
 def test_bt_cls_01_base_classes(fixture_classes_json):

tests/test_augmentation.py

@@ -18,15 +18,7 @@ from tests.conftest import apply_constants_patch
 
 
 def _patch_augmentation_paths(monkeypatch, base: Path):
-    import augmentation as aug
-    import constants as c
-
     apply_constants_patch(monkeypatch, base)
-    monkeypatch.setattr(aug, "data_images_dir", c.data_images_dir)
-    monkeypatch.setattr(aug, "data_labels_dir", c.data_labels_dir)
-    monkeypatch.setattr(aug, "processed_images_dir", c.processed_images_dir)
-    monkeypatch.setattr(aug, "processed_labels_dir", c.processed_labels_dir)
-    monkeypatch.setattr(aug, "processed_dir", c.processed_dir)
 
 
 def _seed():
@@ -210,8 +202,8 @@ def test_bt_aug_07_full_pipeline_five_images_forty_outputs(
     import constants as c
     from augmentation import Augmentator
 
-    img_dir = Path(c.data_images_dir)
-    lbl_dir = Path(c.data_labels_dir)
+    img_dir = Path(c.config.data_images_dir)
+    lbl_dir = Path(c.config.data_labels_dir)
     img_dir.mkdir(parents=True, exist_ok=True)
     lbl_dir.mkdir(parents=True, exist_ok=True)
     src_img, src_lbl = sample_images_labels(5)
@@ -220,8 +212,8 @@ def test_bt_aug_07_full_pipeline_five_images_forty_outputs(
     for p in src_lbl.glob("*.txt"):
         shutil.copy2(p, lbl_dir / p.name)
     Augmentator().augment_annotations()
-    proc_img = Path(c.processed_images_dir)
-    proc_lbl = Path(c.processed_labels_dir)
+    proc_img = Path(c.config.processed_images_dir)
+    proc_lbl = Path(c.config.processed_labels_dir)
     assert len(list(proc_img.glob("*.jpg"))) == 40
     assert len(list(proc_lbl.glob("*.txt"))) == 40
 
@@ -233,10 +225,10 @@ def test_bt_aug_08_skips_already_processed(tmp_path, monkeypatch, sample_images_
     import constants as c
     from augmentation import Augmentator
 
-    img_dir = Path(c.data_images_dir)
-    lbl_dir = Path(c.data_labels_dir)
-    proc_img = Path(c.processed_images_dir)
-    proc_lbl = Path(c.processed_labels_dir)
+    img_dir = Path(c.config.data_images_dir)
+    lbl_dir = Path(c.config.data_labels_dir)
+    proc_img = Path(c.config.processed_images_dir)
+    proc_lbl = Path(c.config.processed_labels_dir)
     img_dir.mkdir(parents=True, exist_ok=True)
     lbl_dir.mkdir(parents=True, exist_ok=True)
     proc_img.mkdir(parents=True, exist_ok=True)

tests/test_augmentation_nonfunc.py

@@ -20,15 +20,7 @@ if "matplotlib" not in sys.modules:
 
 
 def _patch_augmentation_paths(monkeypatch, base: Path):
-    import augmentation as aug
-    import constants as c
-
     apply_constants_patch(monkeypatch, base)
-    monkeypatch.setattr(aug, "data_images_dir", c.data_images_dir)
-    monkeypatch.setattr(aug, "data_labels_dir", c.data_labels_dir)
-    monkeypatch.setattr(aug, "processed_images_dir", c.processed_images_dir)
-    monkeypatch.setattr(aug, "processed_labels_dir", c.processed_labels_dir)
-    monkeypatch.setattr(aug, "processed_dir", c.processed_dir)
 
 
 def _augment_annotation_with_total(monkeypatch):
@@ -58,8 +50,8 @@ def test_rt_aug_01_corrupted_image_skipped(
     import constants as c
     from augmentation import Augmentator
 
-    img_dir = Path(c.data_images_dir)
-    lbl_dir = Path(c.data_labels_dir)
+    img_dir = Path(c.config.data_images_dir)
+    lbl_dir = Path(c.config.data_labels_dir)
     img_dir.mkdir(parents=True, exist_ok=True)
     lbl_dir.mkdir(parents=True, exist_ok=True)
     stem = sorted(fixture_images_dir.glob("*.jpg"))[0].stem
@@ -68,7 +60,7 @@ def test_rt_aug_01_corrupted_image_skipped(
     raw = (fixture_images_dir / f"{stem}.jpg").read_bytes()[:200]
     (img_dir / "corrupted_trunc.jpg").write_bytes(raw)
     Augmentator().augment_annotations()
-    proc_img = Path(c.processed_images_dir)
+    proc_img = Path(c.config.processed_images_dir)
     assert len(list(proc_img.glob("*.jpg"))) == 8
 
 
@@ -79,8 +71,8 @@ def test_rt_aug_02_missing_label_no_crash(tmp_path, monkeypatch, fixture_images_
     import constants as c
     from augmentation import Augmentator
 
-    img_dir = Path(c.data_images_dir)
-    lbl_dir = Path(c.data_labels_dir)
+    img_dir = Path(c.config.data_images_dir)
+    lbl_dir = Path(c.config.data_labels_dir)
     img_dir.mkdir(parents=True, exist_ok=True)
     lbl_dir.mkdir(parents=True, exist_ok=True)
     stem = "no_label_here"
@@ -88,7 +80,7 @@ def test_rt_aug_02_missing_label_no_crash(tmp_path, monkeypatch, fixture_images_
     aug = Augmentator()
     aug.total_images_to_process = 1
     aug.augment_annotation(SimpleNamespace(name=f"{stem}.jpg"))
-    assert len(list(Path(c.processed_images_dir).glob("*.jpg"))) == 0
+    assert len(list(Path(c.config.processed_images_dir).glob("*.jpg"))) == 0
 
 
 @pytest.mark.resilience

tests/test_dataset_formation.py

@@ -55,8 +55,8 @@ def _prepare_form_dataset(
     constants_patch(tmp_path)
     import train
 
-    proc_img = Path(c_mod.processed_images_dir)
-    proc_lbl = Path(c_mod.processed_labels_dir)
+    proc_img = Path(c_mod.config.processed_images_dir)
+    proc_lbl = Path(c_mod.config.processed_labels_dir)
     proc_img.mkdir(parents=True, exist_ok=True)
     proc_lbl.mkdir(parents=True, exist_ok=True)
 
@@ -69,14 +69,8 @@ def _prepare_form_dataset(
         if stem in corrupt_stems:
             dst.write_text("0 1.5 0.5 0.1 0.1\n", encoding="utf-8")
 
-    today_ds = osp.join(c_mod.datasets_dir, train.today_folder)
-    monkeypatch.setattr(train, "today_dataset", today_ds)
-    monkeypatch.setattr(train, "processed_images_dir", c_mod.processed_images_dir)
-    monkeypatch.setattr(train, "processed_labels_dir", c_mod.processed_labels_dir)
-    monkeypatch.setattr(train, "corrupted_images_dir", c_mod.corrupted_images_dir)
-    monkeypatch.setattr(train, "corrupted_labels_dir", c_mod.corrupted_labels_dir)
-    monkeypatch.setattr(train, "datasets_dir", c_mod.datasets_dir)
-    return train
+    today_ds = osp.join(c_mod.config.datasets_dir, train.today_folder)
+    return train, today_ds
 
 
 def _count_jpg(p):
@@ -90,7 +84,7 @@ def test_bt_dsf_01_split_ratio_70_20_10(
     fixture_images_dir,
     fixture_labels_dir,
 ):
-    train = _prepare_form_dataset(
+    train, today_ds = _prepare_form_dataset(
         monkeypatch,
         tmp_path,
         constants_patch,
@@ -100,10 +94,9 @@ def test_bt_dsf_01_split_ratio_70_20_10(
         set(),
     )
     train.form_dataset()
-    base = train.today_dataset
-    assert _count_jpg(Path(base, "train", "images")) == 70
-    assert _count_jpg(Path(base, "valid", "images")) == 20
-    assert _count_jpg(Path(base, "test", "images")) == 10
+    assert _count_jpg(Path(today_ds, "train", "images")) == 70
+    assert _count_jpg(Path(today_ds, "valid", "images")) == 20
+    assert _count_jpg(Path(today_ds, "test", "images")) == 10
 
 
 def test_bt_dsf_02_six_subdirectories(
@@ -113,7 +106,7 @@ def test_bt_dsf_02_six_subdirectories(
     fixture_images_dir,
     fixture_labels_dir,
 ):
-    train = _prepare_form_dataset(
+    train, today_ds = _prepare_form_dataset(
         monkeypatch,
         tmp_path,
         constants_patch,
@@ -123,7 +116,7 @@ def test_bt_dsf_02_six_subdirectories(
         set(),
     )
     train.form_dataset()
-    base = Path(train.today_dataset)
+    base = Path(today_ds)
     assert (base / "train" / "images").is_dir()
     assert (base / "train" / "labels").is_dir()
     assert (base / "valid" / "images").is_dir()
@@ -139,7 +132,7 @@ def test_bt_dsf_03_total_files_one_hundred(
     fixture_images_dir,
     fixture_labels_dir,
 ):
-    train = _prepare_form_dataset(
+    train, today_ds = _prepare_form_dataset(
         monkeypatch,
         tmp_path,
         constants_patch,
@@ -149,11 +142,10 @@ def test_bt_dsf_03_total_files_one_hundred(
         set(),
     )
     train.form_dataset()
-    base = train.today_dataset
     n = (
-        _count_jpg(Path(base, "train", "images"))
-        + _count_jpg(Path(base, "valid", "images"))
-        + _count_jpg(Path(base, "test", "images"))
+        _count_jpg(Path(today_ds, "train", "images"))
+        + _count_jpg(Path(today_ds, "valid", "images"))
+        + _count_jpg(Path(today_ds, "test", "images"))
     )
     assert n == 100
 
@@ -167,7 +159,7 @@ def test_bt_dsf_04_corrupted_labels_quarantined(
 ):
     stems = [p.stem for p in sorted(fixture_images_dir.glob("*.jpg"))[:100]]
     corrupt = set(stems[:5])
-    train = _prepare_form_dataset(
+    train, today_ds = _prepare_form_dataset(
         monkeypatch,
         tmp_path,
         constants_patch,
@@ -177,15 +169,14 @@ def test_bt_dsf_04_corrupted_labels_quarantined(
         corrupt,
     )
     train.form_dataset()
-    base = train.today_dataset
     split_total = (
-        _count_jpg(Path(base, "train", "images"))
-        + _count_jpg(Path(base, "valid", "images"))
-        + _count_jpg(Path(base, "test", "images"))
+        _count_jpg(Path(today_ds, "train", "images"))
+        + _count_jpg(Path(today_ds, "valid", "images"))
+        + _count_jpg(Path(today_ds, "test", "images"))
     )
     assert split_total == 95
-    assert _count_jpg(c_mod.corrupted_images_dir) == 5
-    assert len(list(Path(c_mod.corrupted_labels_dir).glob("*.txt"))) == 5
+    assert _count_jpg(c_mod.config.corrupted_images_dir) == 5
+    assert len(list(Path(c_mod.config.corrupted_labels_dir).glob("*.txt"))) == 5
 
 
 @pytest.mark.resilience
@@ -196,7 +187,7 @@ def test_rt_dsf_01_empty_processed_no_crash(
     fixture_images_dir,
     fixture_labels_dir,
 ):
-    train = _prepare_form_dataset(
+    train, today_ds = _prepare_form_dataset(
         monkeypatch,
         tmp_path,
         constants_patch,
@@ -206,8 +197,7 @@ def test_rt_dsf_01_empty_processed_no_crash(
         set(),
     )
     train.form_dataset()
-    base = Path(train.today_dataset)
-    assert base.is_dir()
+    assert Path(today_ds).is_dir()
 
 
 @pytest.mark.resource_limit
@@ -225,7 +215,7 @@ def test_rl_dsf_02_no_filename_duplication_across_splits(
     fixture_images_dir,
     fixture_labels_dir,
 ):
-    train = _prepare_form_dataset(
+    train, today_ds = _prepare_form_dataset(
         monkeypatch,
         tmp_path,
         constants_patch,
@@ -235,7 +225,7 @@ def test_rl_dsf_02_no_filename_duplication_across_splits(
         set(),
     )
     train.form_dataset()
-    base = Path(train.today_dataset)
+    base = Path(today_ds)
     names = []
     for split in ("train", "valid", "test"):
         for f in (base / split / "images").glob("*.jpg"):

tests/test_infrastructure.py

@@ -54,6 +54,6 @@ def test_empty_label_file(empty_label):
 
 def test_constants_patch_uses_tmp(constants_patch, tmp_path):
     constants_patch(tmp_path)
-    assert c.azaion.startswith(str(tmp_path))
-    assert c.data_dir.startswith(str(tmp_path))
-    assert c.CURRENT_ONNX_MODEL.startswith(str(tmp_path))
+    assert c.config.azaion.startswith(str(tmp_path))
+    assert c.config.data_dir.startswith(str(tmp_path))
+    assert c.config.current_onnx_model.startswith(str(tmp_path))

tests/test_training_e2e.py

@@ -0,0 +1,113 @@
+import sys
+import types
+import importlib
+import shutil
+from os import path as osp
+from pathlib import Path
+
+import pytest
+
+for _n in ("boto3", "netron", "requests"):
+    if _n not in sys.modules:
+        sys.modules[_n] = types.ModuleType(_n)
+
+for _k in [k for k in sys.modules if k == "ultralytics" or k.startswith("ultralytics.")]:
+    del sys.modules[_k]
+from ultralytics import YOLO
+
+for _m in ("exports", "train"):
+    if _m in sys.modules:
+        importlib.reload(sys.modules[_m])
+
+import constants as c
+import train as train_mod
+import exports as exports_mod
+
+_PROJECT_ROOT = Path(__file__).resolve().parent.parent
+_DATASET_IMAGES = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/images"
+_DATASET_LABELS = _PROJECT_ROOT / "_docs/00_problem/input_data/dataset/labels"
+_CONFIG_TEST = _PROJECT_ROOT / "config.test.yaml"
+
+
+@pytest.fixture(scope="module")
+def e2e_result(tmp_path_factory):
+    base = tmp_path_factory.mktemp("e2e")
+
+    old_config = c.config
+    c.config = c.Config.from_yaml(str(_CONFIG_TEST), root=str(base / "azaion"))
+
+    data_img = Path(c.config.data_images_dir)
+    data_lbl = Path(c.config.data_labels_dir)
+    data_img.mkdir(parents=True)
+    data_lbl.mkdir(parents=True)
+    Path(c.config.models_dir).mkdir(parents=True)
+
+    for img in sorted(_DATASET_IMAGES.glob("*.jpg")):
+        shutil.copy2(img, data_img / img.name)
+        lbl = _DATASET_LABELS / f"{img.stem}.txt"
+        if lbl.exists():
+            shutil.copy2(lbl, data_lbl / lbl.name)
+
+    from augmentation import Augmentator
+    Augmentator().augment_annotations()
+
+    train_mod.train_dataset()
+
+    exports_mod.export_onnx(c.config.current_pt_model)
+    exports_mod.export_coreml(c.config.current_pt_model)
+
+    today_ds = osp.join(c.config.datasets_dir, train_mod.today_folder)
+
+    yield {
+        "today_dataset": today_ds,
+    }
+
+    c.config = old_config
+
+
+@pytest.mark.e2e
+class TestTrainingPipeline:
+    def test_augmentation_produced_output(self, e2e_result):
+        proc = Path(c.config.processed_images_dir)
+        assert len(list(proc.glob("*.jpg"))) == 800
+
+    def test_dataset_formed(self, e2e_result):
+        base = Path(e2e_result["today_dataset"])
+        for split in ("train", "valid", "test"):
+            assert (base / split / "images").is_dir()
+            assert (base / split / "labels").is_dir()
+        total = sum(
+            len(list((base / s / "images").glob("*.jpg")))
+            for s in ("train", "valid", "test")
+        )
+        assert total == 800
+
+    def test_data_yaml_created(self, e2e_result):
+        yaml_path = Path(e2e_result["today_dataset"]) / "data.yaml"
+        assert yaml_path.exists()
+        content = yaml_path.read_text()
+        assert "nc: 80" in content
+        assert "train:" in content
+        assert "val:" in content
+
+    def test_training_produces_pt(self, e2e_result):
+        pt = Path(c.config.current_pt_model)
+        assert pt.exists()
+        assert pt.stat().st_size > 0
+
+    def test_export_onnx(self, e2e_result):
+        p = Path(c.config.current_onnx_model)
+        assert p.exists()
+        assert p.suffix == ".onnx"
+        assert p.stat().st_size > 0
+
+    def test_export_coreml(self, e2e_result):
+        pkgs = list(Path(c.config.models_dir).glob("*.mlpackage"))
+        assert len(pkgs) >= 1
+
+    def test_onnx_inference(self, e2e_result):
+        onnx_model = YOLO(c.config.current_onnx_model)
+        img = sorted(_DATASET_IMAGES.glob("*.jpg"))[0]
+        results = onnx_model.predict(source=str(img), imgsz=c.config.export.onnx_imgsz, verbose=False)
+        assert len(results) == 1
+        assert results[0].boxes is not None