diff --git a/mteb/evaluation/evaluators/Image/Any2AnyMultiChoiceEvaluator.py b/mteb/evaluation/evaluators/Image/Any2AnyMultiChoiceEvaluator.py
index 5fdbb112f3..c69f0153a2 100644
--- a/mteb/evaluation/evaluators/Image/Any2AnyMultiChoiceEvaluator.py
+++ b/mteb/evaluation/evaluators/Image/Any2AnyMultiChoiceEvaluator.py
@@ -57,7 +57,8 @@ def __getitem__(self, idx):
             image = image
         if image.mode != "RGB":
             image = image.convert("RGB")
-        image = self.transform(image)
+        if self.transform is not None:
+            image = self.transform(image)
         return image
 
 
@@ -105,6 +106,7 @@ def search(
         qrels: Dataset,
         top_k: int,
         score_function: str,
+        task_name: str | None = None,
         return_sorted: bool = False,
         **kwargs,
     ) -> dict[str, dict[str, float]]:
@@ -122,7 +124,9 @@ def search(
         if q_modality == "text":
             query_texts = queries["text"]
             query_embeddings = self.model.get_text_embeddings(
-                texts=query_texts, batch_size=self.encode_kwargs["batch_size"]
+                texts=query_texts,
+                task_name=task_name,
+                batch_size=self.encode_kwargs["batch_size"],
             )
         else:
             queries_dataset = ImageDataset(
@@ -139,6 +143,7 @@ def search(
                 query_embeddings = self.model.get_image_embeddings(
                     images=query_image_dataloader,
                     batch_size=self.encode_kwargs["batch_size"],
+                    task_name=task_name,
                 )
             elif q_modality == "image,text":
                 query_texts = queries["text"]
@@ -146,6 +151,7 @@ def search(
                     texts=query_texts,
                     images=query_image_dataloader,
                     batch_size=self.encode_kwargs["batch_size"],
+                    task_name=task_name,
                 )
             else:
                 raise ValueError(f"Unsupported modality: {q_modality}")
@@ -189,6 +195,7 @@ def search(
                     sub_corpus_embeddings = self.model.get_image_embeddings(
                         images=corpus_image_dataloader,
                         batch_size=self.encode_kwargs["batch_size"],
+                        task_name=task_name,
                     )
                 elif corpus_modality == "image,text":
                     corpus_texts = chunk["text"]
@@ -196,6 +203,7 @@ def search(
                         texts=corpus_texts,
                         images=corpus_image_dataloader,
                         batch_size=self.encode_kwargs["batch_size"],
+                        task_name=task_name,
                     )
                 else:
                     raise ValueError(f"Unsupported modality: {corpus_modality}")
@@ -301,7 +309,7 @@ def __call__(
             qrels,
             self.top_k,
             self.score_function,
-            prompt_name=self.task_name,  # type: ignore
+            task_name=self.task_name,  # type: ignore
         )
 
     @staticmethod
diff --git a/mteb/evaluation/evaluators/Image/ImageTextPairClassificationEvaluator.py b/mteb/evaluation/evaluators/Image/ImageTextPairClassificationEvaluator.py
index 7e3d84bb87..f3188f7753 100644
--- a/mteb/evaluation/evaluators/Image/ImageTextPairClassificationEvaluator.py
+++ b/mteb/evaluation/evaluators/Image/ImageTextPairClassificationEvaluator.py
@@ -134,11 +134,15 @@ def __call__(
             images = [img for images in images_list for img in images]
             texts = [txt for texts in texts_list for txt in texts]
             images_emb = F.normalize(
-                model.get_image_embeddings(images, batch_size=len(images)),
+                model.get_image_embeddings(
+                    images, batch_size=len(images), task_name=self.task_name
+                ),
                 dim=-1,
             ).view(len(batch), num_images_per_sample, -1)
             texts_emb = F.normalize(
-                model.get_text_embeddings(texts, batch_size=len(texts)),
+                model.get_text_embeddings(
+                    texts, batch_size=len(texts), task_name=self.task_name
+                ),
                 dim=-1,
             ).view(len(batch), num_texts_per_sample, -1)
             for i in range(len(batch)):
diff --git a/tests/test_benchmark/test_benchmark.py b/tests/test_benchmark/test_benchmark.py
index d7357664fe..826610657f 100644
--- a/tests/test_benchmark/test_benchmark.py
+++ b/tests/test_benchmark/test_benchmark.py
@@ -35,7 +35,7 @@
     MockRerankingTask,
     MockRetrievalTask,
 )
-from .task_grid import MOCK_TASK_TEST_GRID
+from .task_grid import MOCK_MIEB_TASK_GRID, MOCK_TASK_TEST_GRID
 
 logging.basicConfig(level=logging.INFO)
 
@@ -175,6 +175,32 @@ def encode(self, sentences, prompt_name: str | None = None, **kwargs):
     )
 
 
+@pytest.mark.parametrize("task_name", MOCK_TASK_TEST_GRID + MOCK_MIEB_TASK_GRID)
+def test_task_name_passed_encoder(task_name: mteb.AbsTask, tmp_path: Path):
+    """Test that all tasks correctly pass down the task_name to the encoder."""
+    _task_name = (
+        task_name.metadata.name if isinstance(task_name, mteb.AbsTask) else task_name
+    )
+
+    class MockEncoderWithInstructions(mteb.Encoder):
+        def encode(self, sentences, task_name: str | None = None, **kwargs):
+            assert task_name == _task_name
+            return np.zeros((len(sentences), 10))
+
+    if isinstance(task_name, mteb.AbsTask):
+        tasks = [task_name]
+    else:
+        tasks = mteb.get_tasks(tasks=[task_name])
+
+    eval = mteb.MTEB(tasks=tasks)
+
+    eval.run(
+        MockEncoderWithInstructions(),
+        output_folder=tmp_path.as_posix(),
+        overwrite_results=True,
+    )
+
+
 @pytest.mark.parametrize("model", [MockNumpyEncoder()])
 def test_run_using_benchmark(model: mteb.Encoder, tmp_path: Path):
     """Test that a benchmark object can be run using the MTEB class."""