diff --git a/tests/models/mmdet/test_timm_backbones.py b/tests/models/mmdet/test_timm_backbones.py
new file mode 100644
index 000000000..6c60764ef
--- /dev/null
+++ b/tests/models/mmdet/test_timm_backbones.py
@@ -0,0 +1,55 @@
+import pytest
+from icevision.all import *
+
+
+@pytest.mark.parametrize(
+    "ds, model_type",
+    [
+        (
+            "fridge_ds",
+            models.mmdet.retinanet,
+        ),
+    ],
+)
+class TestTimmBackbones:
+    def dls_model(self, ds, model_type, samples_source, request):
+        train_ds, valid_ds = request.getfixturevalue(ds)
+        train_dl = model_type.train_dl(train_ds, batch_size=2)
+        valid_dl = model_type.valid_dl(valid_ds, batch_size=2)
+
+        # backbone = model_type.backbones.mmdet.resnet50_fpn_1x()
+        backbone = model_type.backbones.timm.mobilenet.mobilenetv3_large_100
+        backbone.config_path = samples_source / backbone.config_path
+
+        model = model_type.model(backbone=backbone, num_classes=5)
+
+        return train_dl, valid_dl, model
+
+    def test_mmdet_bbox_models_fastai(self, ds, model_type, samples_source, request):
+        train_dl, valid_dl, model = self.dls_model(
+            ds, model_type, samples_source, request
+        )
+
+        learn = model_type.fastai.learner(
+            dls=[train_dl, valid_dl], model=model, splitter=fastai.trainable_params
+        )
+        learn.fine_tune(1, 3e-4)
+
+    def test_mmdet_bbox_models_light(self, ds, model_type, samples_source, request):
+        train_dl, valid_dl, model = self.dls_model(
+            ds, model_type, samples_source, request
+        )
+
+        class LitModel(model_type.lightning.ModelAdapter):
+            def configure_optimizers(self):
+                return Adam(self.parameters(), lr=1e-4)
+
+        light_model = LitModel(model)
+        trainer = pl.Trainer(
+            max_epochs=1,
+            weights_summary=None,
+            num_sanity_val_steps=0,
+            logger=False,
+            checkpoint_callback=False,
+        )
+        trainer.fit(light_model, train_dl, valid_dl)