Remove YOLOv5, YOLOv7, YOLOv9, and YOLOv10 upload support

CHANGELOG.md

@@ -28,6 +28,12 @@ All notable changes to this project will be documented in this file.
   hidden alias of `--existing-annotations preserve`
   ([#570](https://github.com/roboflow/roboflow-python/pull/570)).
 
+### Removed
+
+- Custom model weight uploads no longer support YOLOv5, YOLOv7, YOLOv9, or
+  YOLOv10. Existing models remain available for inference, and YOLO dataset
+  formats remain supported ([#572](https://github.com/roboflow/roboflow-python/pull/572)).
+
 ## 1.7.3
 
 ### Added

README.md

@@ -164,8 +164,8 @@ workspace.upload_dataset(
 
 version = project.version("VERSION_NUMBER")
 
-# upload model weights - yolov10
-version.deploy(model_type="yolov10", model_path=f”{HOME}/runs/detect/train/”, filename="weights.pt")
+# upload model weights - YOLOv8 Nano
+version.deploy(model_type="yolov8n", model_path=f"{HOME}/runs/detect/train/", filename="weights/best.pt")
 
 # run inference (a version may own several trained models; models() returns all of them)
 model = version.models()[0]

docs/index.md

@@ -91,8 +91,8 @@ workspace.upload_dataset(
 
 version = project.version("VERSION_NUMBER")
 
-# upload model weights - yolov10
-version.deploy(model_type="yolov10", model_path=f”{HOME}/runs/detect/train/”, filename="weights.pt")
+# upload model weights - YOLOv8 Nano
+version.deploy(model_type="yolov8n", model_path=f"{HOME}/runs/detect/train/", filename="weights/best.pt")
 
 # run inference (a version may own several trained models; models() returns all of them)
 model = version.models()[0]

roboflow/cli/handlers/model.py

@@ -91,7 +91,7 @@ def model_infer(
 @model_app.command("upload")
 def upload_model(
     ctx: typer.Context,
-    model_type: Annotated[str, typer.Option("-t", "--type", help="Model type (e.g. yolov8, yolov5)")],
+    model_type: Annotated[str, typer.Option("-t", "--type", help="Model type (e.g. yolov8n, yolo26n, rfdetr-nano)")],
     model_path: Annotated[str, typer.Option("-m", "--model-path", help="Path to the trained model file")],
     project: Annotated[
         Optional[list[str]], typer.Option("-p", "--project", help="Project ID (repeatable for multi-project deploy)")

roboflow/util/model_processor.py

@@ -54,12 +54,7 @@
 from roboflow.util.versions import get_wrong_dependencies_versions, normalize_yolo_model_type
 
 SUPPORTED_MODELS = (
-    "yolov5",
-    "yolov7",
-    "yolov7-seg",
     "yolov8",
-    "yolov9",
-    "yolov10",
     "yolov11",
     "yolov12",
     "yolo26",
@@ -116,12 +111,11 @@
 DEFAULT_WEIGHTS_FILENAME = "weights/best.pt"
 
 # YOLO families Roboflow rejects without a size suffix (e.g. `yolov8` must be
-# `yolov8n`/`yolov8s`/...). Legacy yolov5/7/9 go through the opt.yaml path and are
-# intentionally excluded.
-ULTRALYTICS_YOLO_FAMILIES = ("yolov8", "yolov10", "yolov11", "yolov12", "yolo26")
+# `yolov8n`/`yolov8s`/...).
+ULTRALYTICS_YOLO_FAMILIES = ("yolov8", "yolov11", "yolov12", "yolo26")
 
 # Canonical (depth_multiple, width_multiple) -> size letter for the classic YOLO
-# scaling (v5/v8/v9/v10). Newer families (v11+) instead store an explicit ``scale``
+# scaling in v8. Newer families (v11+) instead store an explicit ``scale``
 # letter in the model yaml, which is read first.
 YOLO_DEPTH_WIDTH_TO_SIZE = {
     (0.33, 0.25): "n",
@@ -552,19 +546,6 @@ def _load_checkpoint(torch_module: Any, checkpoint_path: Path, *, map_location:
     return torch_module.load(checkpoint_path, **kwargs)
 
 
-def _legacy_yolo_args(opts: dict[str, Any], opt_path: Path) -> dict[str, Any]:
-    """Return required legacy YOLO upload args from opt.yaml."""
-    if "imgsz" in opts:
-        image_size = opts["imgsz"]
-    elif "img_size" in opts:
-        image_size = opts["img_size"]
-    else:
-        raise ModelPackagingError(f"{opt_path} is missing required key 'imgsz' or 'img_size'.")
-    if "batch_size" not in opts:
-        raise ModelPackagingError(f"{opt_path} is missing required key 'batch_size'.")
-    return {"imgsz": image_size, "batch": opts["batch_size"]}
-
-
 def _infer_yolo_size(model_instance: Any) -> str | None:
     """Infer a YOLO size letter (n/s/m/l/x/...) from a loaded checkpoint.
 
@@ -703,8 +684,6 @@ def _process_yolo(
             allow_mismatch=allow_dependency_mismatch,
             warnings=warnings,
         )
-    elif "yolov10" in model_type:
-        ultralytics = _import_required_module("ultralytics", "pip install ultralytics")
     elif "yolov11" in model_type:
         ultralytics = _import_required_module("ultralytics", "pip install 'ultralytics>=8.3.0'")
         _check_dependency_version(
@@ -758,50 +737,33 @@ def _process_yolo(
             )
 
     class_names = _class_names_from_model_instance(model_instance)
-    if any(name in model_type for name in ULTRALYTICS_YOLO_FAMILIES):
-        if ultralytics is None:
-            ultralytics = _import_required_module("ultralytics", "pip install ultralytics")
-        model_yaml = _require_model_attr(model_instance, "yaml", model_type)
-        if (
-            "-cls" in model_type
-            or model_type.startswith("yolov10")
-            or model_type.startswith("yolov11")
-            or model_type.startswith("yolov12")
-            or model_type.startswith("yolo26")
-        ):
-            if not isinstance(model_yaml, dict) or "nc" not in model_yaml:
-                raise ModelPackagingError(
-                    f"The {model_type} checkpoint's model config (model.yaml) is missing 'nc'; "
-                    "it does not look like a complete Ultralytics training checkpoint."
-                )
-            nc = model_yaml["nc"]
-            args = _require_checkpoint_field(checkpoint, "train_args", model_type)
-        else:
-            nc = _require_model_attr(model_instance, "nc", model_type)
-            args = _require_model_attr(model_instance, "args", model_type)
-        model_artifacts: dict[str, Any] = {
-            "names": class_names,
-            "yaml": model_yaml,
-            "nc": nc,
-            "args": _filtered_args(args),
-            "ultralytics_version": ultralytics.__version__,
-            "model_type": model_type,
-        }
+    if ultralytics is None:
+        ultralytics = _import_required_module("ultralytics", "pip install ultralytics")
+    model_yaml = _require_model_attr(model_instance, "yaml", model_type)
+    if (
+        "-cls" in model_type
+        or model_type.startswith("yolov11")
+        or model_type.startswith("yolov12")
+        or model_type.startswith("yolo26")
+    ):
+        if not isinstance(model_yaml, dict) or "nc" not in model_yaml:
+            raise ModelPackagingError(
+                f"The {model_type} checkpoint's model config (model.yaml) is missing 'nc'; "
+                "it does not look like a complete Ultralytics training checkpoint."
+            )
+        nc = model_yaml["nc"]
+        args = _require_checkpoint_field(checkpoint, "train_args", model_type)
     else:
-        # yolov5 / yolov7 / yolov9 read their upload args from opt.yaml
-        opt_path = model_path / "opt.yaml"
-        if not opt_path.exists():
-            raise MissingFileError(f"You must provide an opt.yaml file at '{opt_path}' for {model_type} uploads.")
-        with opt_path.open() as stream:
-            opts = yaml.safe_load(stream) or {}
-        model_artifacts = {
-            "names": class_names,
-            "nc": _require_model_attr(model_instance, "nc", model_type),
-            "args": _legacy_yolo_args(opts, opt_path),
-            "model_type": model_type,
-        }
-        if hasattr(model_instance, "yaml"):
-            model_artifacts["yaml"] = model_instance.yaml
+        nc = _require_model_attr(model_instance, "nc", model_type)
+        args = _require_model_attr(model_instance, "args", model_type)
+    model_artifacts: dict[str, Any] = {
+        "names": class_names,
+        "yaml": model_yaml,
+        "nc": nc,
+        "args": _filtered_args(args),
+        "ultralytics_version": ultralytics.__version__,
+        "model_type": model_type,
+    }
 
     (build_dir / "model_artifacts.json").write_text(json.dumps(model_artifacts))
     torch.save(model_instance.state_dict(), build_dir / "state_dict.pt")

tests/util/test_model_processor.py

@@ -25,7 +25,6 @@
     _filtered_args,
     _infer_yolo_size,
     _is_ptl_checkpoint,
-    _legacy_yolo_args,
     _require_rfdetr,
     _resolve_rfdetr_variant,
     _resolve_yolo_size,
@@ -58,7 +57,7 @@ def test_detect_defaults(self):
     def test_segment(self):
         self.assertEqual(task_of_model_type("yolov11-seg"), TASK_SEG)
         self.assertEqual(task_of_model_type("rfdetr-seg-medium"), TASK_SEG)
-        self.assertEqual(task_of_model_type("yolov7-seg"), TASK_SEG)
+        self.assertEqual(task_of_model_type("yolov8-seg"), TASK_SEG)
 
     def test_pose(self):
         self.assertEqual(task_of_model_type("yolov11-pose"), TASK_POSE)
@@ -196,22 +195,6 @@ def test_unknown_project_type_is_ignored(self):
         validate_model_type_for_project("yolov8", "some-new-type", "widgets")
 
 
-class LegacyYoloArgsTest(unittest.TestCase):
-    def test_reports_missing_batch_size(self):
-        with self.assertRaises(ModelPackagingError) as ctx:
-            _legacy_yolo_args({"imgsz": 640}, Path("opt.yaml"))
-        self.assertIn("batch_size", str(ctx.exception))
-
-    def test_reports_missing_image_size(self):
-        with self.assertRaises(ModelPackagingError) as ctx:
-            _legacy_yolo_args({"batch_size": 8}, Path("opt.yaml"))
-        self.assertIn("imgsz", str(ctx.exception))
-
-    def test_accepts_either_image_size_key(self):
-        self.assertEqual(_legacy_yolo_args({"imgsz": 640, "batch_size": 8}, Path("x")), {"imgsz": 640, "batch": 8})
-        self.assertEqual(_legacy_yolo_args({"img_size": 416, "batch_size": 4}, Path("x")), {"imgsz": 416, "batch": 4})
-
-
 class _FakeYoloModel:
     def __init__(self, yaml):
         self.yaml = yaml
@@ -275,15 +258,15 @@ def test_keeps_matching_sized_type_without_warning(self):
 
     def test_bare_family_uninferable_raises_by_default(self):
         with self.assertRaises(SizeMismatchError):
-            _resolve_yolo_size("yolov10", _FakeYoloModel({}), [])
+            _resolve_yolo_size("yolov8", _FakeYoloModel({}), [])
 
     def test_bare_family_uninferable_proceeds_under_allow_mismatch(self):
         # The interactive retry sets allow_mismatch=True after the user confirms;
         # this branch must then converge (return) rather than raise forever.
         warnings: list = []
         self.assertEqual(
-            _resolve_yolo_size("yolov10", _FakeYoloModel({}), warnings, allow_mismatch=True),
-            "yolov10",
+            _resolve_yolo_size("yolov8", _FakeYoloModel({}), warnings, allow_mismatch=True),
+            "yolov8",
         )
         self.assertTrue(warnings and "bare family name" in warnings[0])
 
@@ -434,6 +417,18 @@ def test_missing_model_path_raises_missing_file(self):
         with self.assertRaises(MissingFileError):
             package_custom_weights("yolonas", "/nonexistent/path/for/test")
 
+    def test_retired_yolo_families_fail_before_loading_weights(self):
+        with tempfile.TemporaryDirectory() as tmp:
+            for family in ("5", "7", "9", "10"):
+                for model_type in (f"yolov{family}", f"yolov{family}n", f"yolo{family}s-seg"):
+                    with self.subTest(model_type=model_type):
+                        with mock.patch.object(model_processor, "_import_required_module") as import_module:
+                            with mock.patch.object(model_processor, "_load_checkpoint") as load:
+                                with self.assertRaises(UnsupportedModelError):
+                                    package_custom_weights(model_type, tmp)
+                                import_module.assert_not_called()
+                                load.assert_not_called()
+
     def test_family_must_be_a_prefix_not_a_substring(self):
         # 'foo-yolov8n' merely contains a family token; the backend would
         # reject it after upload, so the gate must reject it up front.
@@ -684,7 +679,7 @@ def state_dict(self):
         self.assertIn("yaml", str(ctx.exception))
 
     def test_yolov11_missing_train_args_raises_packaging_error(self):
-        # yolov10/11/12/26 read args from checkpoint["train_args"]; a checkpoint
+        # yolov11/12/26 read args from checkpoint["train_args"]; a checkpoint
         # without it must fail with the ModelPackagingError contract.
         class DetectionModel:
             names = {0: "cat"}