Image Feature Extraction pipeline (#28216)

* Draft pipeline

* Fixup

* Fix docstrings

* Update doctest

* Update pipeline_model_mapping

* Update docstring

* Update tests

* Update src/transformers/pipelines/image_feature_extraction.py

Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>

* Fix docstrings - review comments

* Remove pipeline mapping for composite vision models

* Add to pipeline tests

* Remove for flava (multimodal)

* safe pil import

* Add requirements for pipeline run

* Account for super slow efficientnet

* Review comments

* Fix tests

* Swap order of kwargs

* Use build_pipeline_init_args

* Add back FE pipeline for Vilt

* Include image_processor_kwargs in docstring

* Mark test as flaky

* Update TODO

* Update tests/pipelines/test_pipelines_image_feature_extraction.py

Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

* Add license header

---------

Co-authored-by: Omar Sanseviero <osanseviero@gmail.com>
Co-authored-by: Arthur <48595927+ArthurZucker@users.noreply.github.com>

docs/source/en/main_classes/pipelines.md

@@ -469,6 +469,12 @@ Pipelines available for multimodal tasks include the following.
     - __call__
     - all
 
+### ImageFeatureExtractionPipeline
+
+[[autodoc]] ImageFeatureExtractionPipeline
+    - __call__
+    - all
+
 ### ImageToTextPipeline
 
 [[autodoc]] ImageToTextPipeline

docs/source/ja/main_classes/pipelines.md

@@ -477,6 +477,12 @@ my_pipeline = pipeline(model="xxxx", pipeline_class=MyPipeline)
     - __call__
     - all
 
+### ImageFeatureExtractionPipeline
+
+[[autodoc]] ImageFeatureExtractionPipeline
+    - __call__
+    - all
+
 ### ImageToTextPipeline
 
 [[autodoc]] ImageToTextPipeline

docs/source/zh/main_classes/pipelines.md

@@ -451,6 +451,12 @@ See [`TokenClassificationPipeline`] for all details.
     - __call__
     - all
 
+### ImageFeatureExtractionPipeline
+
+[[autodoc]] ImageFeatureExtractionPipeline
+    - __call__
+    - all
+
 ### ImageToTextPipeline
 
 [[autodoc]] ImageToTextPipeline

src/transformers/__init__.py

@@ -973,6 +973,7 @@ _import_structure = {
         "FeatureExtractionPipeline",
         "FillMaskPipeline",
         "ImageClassificationPipeline",
+        "ImageFeatureExtractionPipeline",
         "ImageSegmentationPipeline",
         "ImageToImagePipeline",
         "ImageToTextPipeline",
@@ -5709,6 +5710,7 @@ if TYPE_CHECKING:
         FeatureExtractionPipeline,
         FillMaskPipeline,
         ImageClassificationPipeline,
+        ImageFeatureExtractionPipeline,
         ImageSegmentationPipeline,
         ImageToImagePipeline,
         ImageToTextPipeline,

src/transformers/pipelines/__init__.py

@@ -66,6 +66,7 @@ from .document_question_answering import DocumentQuestionAnsweringPipeline
 from .feature_extraction import FeatureExtractionPipeline
 from .fill_mask import FillMaskPipeline
 from .image_classification import ImageClassificationPipeline
+from .image_feature_extraction import ImageFeatureExtractionPipeline
 from .image_segmentation import ImageSegmentationPipeline
 from .image_to_image import ImageToImagePipeline
 from .image_to_text import ImageToTextPipeline
@@ -362,6 +363,18 @@ SUPPORTED_TASKS = {
         },
         "type": "image",
     },
+    "image-feature-extraction": {
+        "impl": ImageFeatureExtractionPipeline,
+        "tf": (TFAutoModel,) if is_tf_available() else (),
+        "pt": (AutoModel,) if is_torch_available() else (),
+        "default": {
+            "model": {
+                "pt": ("google/vit-base-patch16-224", "29e7a1e183"),
+                "tf": ("google/vit-base-patch16-224", "29e7a1e183"),
+            }
+        },
+        "type": "image",
+    },
     "image-segmentation": {
         "impl": ImageSegmentationPipeline,
         "tf": (),
@@ -500,6 +513,7 @@ def check_task(task: str) -> Tuple[str, Dict, Any]:
             - `"feature-extraction"`
             - `"fill-mask"`
             - `"image-classification"`
+            - `"image-feature-extraction"`
             - `"image-segmentation"`
             - `"image-to-text"`
             - `"image-to-image"`
@@ -586,6 +600,7 @@ def pipeline(
             - `"feature-extraction"`: will return a [`FeatureExtractionPipeline`].
             - `"fill-mask"`: will return a [`FillMaskPipeline`]:.
             - `"image-classification"`: will return a [`ImageClassificationPipeline`].
+            - `"image-feature-extraction"`: will return an [`ImageFeatureExtractionPipeline`].
             - `"image-segmentation"`: will return a [`ImageSegmentationPipeline`].
             - `"image-to-image"`: will return a [`ImageToImagePipeline`].
             - `"image-to-text"`: will return a [`ImageToTextPipeline`].

src/transformers/pipelines/feature_extraction.py

@@ -14,7 +14,7 @@ from .base import GenericTensor, Pipeline, build_pipeline_init_args
 )
 class FeatureExtractionPipeline(Pipeline):
     """
-    Feature extraction pipeline using no model head. This pipeline extracts the hidden states from the base
+    Feature extraction pipeline uses no model head. This pipeline extracts the hidden states from the base
     transformer, which can be used as features in downstream tasks.
 
     Example:

src/transformers/pipelines/image_feature_extraction.py

@@ -0,0 +1,92 @@
+from typing import Dict
+
+from ..utils import add_end_docstrings, is_vision_available
+from .base import GenericTensor, Pipeline, build_pipeline_init_args
+
+
+if is_vision_available():
+    from ..image_utils import load_image
+
+
+@add_end_docstrings(
+    build_pipeline_init_args(has_image_processor=True),
+    """
+        image_processor_kwargs (`dict`, *optional*):
+                Additional dictionary of keyword arguments passed along to the image processor e.g.
+                {"size": {"height": 100, "width": 100}}
+    """,
+)
+class ImageFeatureExtractionPipeline(Pipeline):
+    """
+    Image feature extraction pipeline uses no model head. This pipeline extracts the hidden states from the base
+    transformer, which can be used as features in downstream tasks.
+
+    Example:
+
+    ```python
+    >>> from transformers import pipeline
+
+    >>> extractor = pipeline(model="google/vit-base-patch16-224", task="image-feature-extraction")
+    >>> result = extractor("https://huggingface.co/datasets/Narsil/image_dummy/raw/main/parrots.png", return_tensors=True)
+    >>> result.shape  # This is a tensor of shape [1, sequence_lenth, hidden_dimension] representing the input image.
+    torch.Size([1, 197, 768])
+    ```
+
+    Learn more about the basics of using a pipeline in the [pipeline tutorial](../pipeline_tutorial)
+
+    This image feature extraction pipeline can currently be loaded from [`pipeline`] using the task identifier:
+    `"image-feature-extraction"`.
+
+    All vision models may be used for this pipeline. See a list of all models, including community-contributed models on
+    [huggingface.co/models](https://huggingface.co/models).
+    """
+
+    def _sanitize_parameters(self, image_processor_kwargs=None, return_tensors=None, **kwargs):
+        preprocess_params = {} if image_processor_kwargs is None else image_processor_kwargs
+        postprocess_params = {"return_tensors": return_tensors} if return_tensors is not None else {}
+
+        if "timeout" in kwargs:
+            preprocess_params["timeout"] = kwargs["timeout"]
+
+        return preprocess_params, {}, postprocess_params
+
+    def preprocess(self, image, timeout=None, **image_processor_kwargs) -> Dict[str, GenericTensor]:
+        image = load_image(image, timeout=timeout)
+        model_inputs = self.image_processor(image, return_tensors=self.framework, **image_processor_kwargs)
+        return model_inputs
+
+    def _forward(self, model_inputs):
+        model_outputs = self.model(**model_inputs)
+        return model_outputs
+
+    def postprocess(self, model_outputs, return_tensors=False):
+        # [0] is the first available tensor, logits or last_hidden_state.
+        if return_tensors:
+            return model_outputs[0]
+        if self.framework == "pt":
+            return model_outputs[0].tolist()
+        elif self.framework == "tf":
+            return model_outputs[0].numpy().tolist()
+
+    def __call__(self, *args, **kwargs):
+        """
+        Extract the features of the input(s).
+
+        Args:
+            images (`str`, `List[str]`, `PIL.Image` or `List[PIL.Image]`):
+                The pipeline handles three types of images:
+
+                - A string containing a http link pointing to an image
+                - A string containing a local path to an image
+                - An image loaded in PIL directly
+
+                The pipeline accepts either a single image or a batch of images, which must then be passed as a string.
+                Images in a batch must all be in the same format: all as http links, all as local paths, or all as PIL
+                images.
+            timeout (`float`, *optional*, defaults to None):
+                The maximum time in seconds to wait for fetching images from the web. If None, no timeout is used and
+                the call may block forever.
+        Return:
+            A nested list of `float`: The features computed by the model.
+        """
+        return super().__call__(*args, **kwargs)

src/transformers/pipelines/image_to_text.py

@@ -1,3 +1,18 @@
+# coding=utf-8
+# Copyright 2024 The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
 from typing import List, Union
 
 from ..utils import (

tests/models/beit/test_modeling_beit.py

@@ -242,7 +242,7 @@ class BeitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": BeitModel,
+            "image-feature-extraction": BeitModel,
             "image-classification": BeitForImageClassification,
             "image-segmentation": BeitForSemanticSegmentation,
         }

tests/models/bit/test_modeling_bit.py

@@ -162,7 +162,7 @@ class BitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
     all_model_classes = (BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
+        {"image-feature-extraction": BitModel, "image-classification": BitForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/blip/test_modeling_blip.py

@@ -429,7 +429,10 @@ class BlipModelTester:
 class BlipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (BlipModel,) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": BlipModel, "image-to-text": BlipForConditionalGeneration}
+        {
+            "feature-extraction": BlipModel,
+            "image-to-text": BlipForConditionalGeneration,
+        }
         if is_torch_available()
         else {}
     )

tests/models/clip/test_modeling_clip.py

@@ -477,7 +477,9 @@ class CLIPModelTester:
 @require_torch
 class CLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (CLIPModel,) if is_torch_available() else ()
-    pipeline_model_mapping = {"feature-extraction": CLIPModel} if is_torch_available() else {}
+    pipeline_model_mapping = (
+        {"feature-extraction": CLIPModel, "image-feature-extraction": CLIPVisionModel} if is_torch_available() else {}
+    )
     fx_compatible = True
     test_head_masking = False
     test_pruning = False

tests/models/conditional_detr/test_modeling_conditional_detr.py

@@ -185,7 +185,7 @@ class ConditionalDetrModelTest(ModelTesterMixin, GenerationTesterMixin, Pipeline
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": ConditionalDetrModel, "object-detection": ConditionalDetrForObjectDetection}
+        {"image-feature-extraction": ConditionalDetrModel, "object-detection": ConditionalDetrForObjectDetection}
         if is_torch_available()
         else {}
     )

tests/models/convnext/test_modeling_convnext.py

@@ -172,7 +172,7 @@ class ConvNextModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
+        {"image-feature-extraction": ConvNextModel, "image-classification": ConvNextForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/convnextv2/test_modeling_convnextv2.py

@@ -180,7 +180,7 @@ class ConvNextV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": ConvNextV2Model, "image-classification": ConvNextV2ForImageClassification}
+        {"image-feature-extraction": ConvNextV2Model, "image-classification": ConvNextV2ForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/cvt/test_modeling_cvt.py

@@ -151,7 +151,7 @@ class CvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
     all_model_classes = (CvtModel, CvtForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
+        {"image-feature-extraction": CvtModel, "image-classification": CvtForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/data2vec/test_modeling_data2vec_vision.py

@@ -178,7 +178,7 @@ class Data2VecVisionModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Te
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": Data2VecVisionModel,
+            "image-feature-extraction": Data2VecVisionModel,
             "image-classification": Data2VecVisionForImageClassification,
             "image-segmentation": Data2VecVisionForSemanticSegmentation,
         }

tests/models/deformable_detr/test_modeling_deformable_detr.py

@@ -191,7 +191,7 @@ class DeformableDetrModelTester:
 class DeformableDetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (DeformableDetrModel, DeformableDetrForObjectDetection) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection}
+        {"image-feature-extraction": DeformableDetrModel, "object-detection": DeformableDetrForObjectDetection}
         if is_torch_available()
         else {}
     )

tests/models/deit/test_modeling_deit.py

@@ -206,7 +206,7 @@ class DeiTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": DeiTModel,
+            "image-feature-extraction": DeiTModel,
             "image-classification": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
         }
         if is_torch_available()

tests/models/deta/test_modeling_deta.py

@@ -217,7 +217,7 @@ class DetaModelTester:
 class DetaModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (DetaModel, DetaForObjectDetection) if is_torchvision_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": DetaModel, "object-detection": DetaForObjectDetection}
+        {"image-feature-extraction": DetaModel, "object-detection": DetaForObjectDetection}
         if is_torchvision_available()
         else {}
     )

tests/models/detr/test_modeling_detr.py

@@ -182,7 +182,7 @@ class DetrModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterMixin
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": DetrModel,
+            "image-feature-extraction": DetrModel,
             "image-segmentation": DetrForSegmentation,
             "object-detection": DetrForObjectDetection,
         }

tests/models/dinat/test_modeling_dinat.py

@@ -207,7 +207,7 @@ class DinatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
+        {"image-feature-extraction": DinatModel, "image-classification": DinatForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/dinov2/test_modeling_dinov2.py

@@ -217,7 +217,7 @@ class Dinov2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": Dinov2Model, "image-classification": Dinov2ForImageClassification}
+        {"image-feature-extraction": Dinov2Model, "image-classification": Dinov2ForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/donut/test_modeling_donut_swin.py

@@ -145,7 +145,7 @@ class DonutSwinModelTester:
 @require_torch
 class DonutSwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (DonutSwinModel,) if is_torch_available() else ()
-    pipeline_model_mapping = {"feature-extraction": DonutSwinModel} if is_torch_available() else {}
+    pipeline_model_mapping = {"image-feature-extraction": DonutSwinModel} if is_torch_available() else {}
     fx_compatible = True
 
     test_pruning = False

tests/models/dpt/test_modeling_dpt.py

@@ -163,7 +163,7 @@ class DPTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     pipeline_model_mapping = (
         {
             "depth-estimation": DPTForDepthEstimation,
-            "feature-extraction": DPTModel,
+            "image-feature-extraction": DPTModel,
             "image-segmentation": DPTForSemanticSegmentation,
         }
         if is_torch_available()

tests/models/efficientformer/test_modeling_efficientformer.py

@@ -190,7 +190,7 @@ class EfficientFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.T
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": EfficientFormerModel,
+            "image-feature-extraction": EfficientFormerModel,
             "image-classification": (
                 EfficientFormerForImageClassification,
                 EfficientFormerForImageClassificationWithTeacher,

tests/models/efficientnet/test_modeling_efficientnet.py

@@ -130,7 +130,7 @@ class EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Test
 
     all_model_classes = (EfficientNetModel, EfficientNetForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
+        {"image-feature-extraction": EfficientNetModel, "image-classification": EfficientNetForImageClassification}
         if is_torch_available()
         else {}
     )
@@ -216,6 +216,12 @@ class EfficientNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.Test
             model = EfficientNetModel.from_pretrained(model_name)
             self.assertIsNotNone(model)
 
+    @is_pipeline_test
+    @require_vision
+    @slow
+    def test_pipeline_image_feature_extraction(self):
+        super().test_pipeline_image_feature_extraction()
+
     @is_pipeline_test
     @require_vision
     @slow

tests/models/focalnet/test_modeling_focalnet.py

@@ -238,7 +238,7 @@ class FocalNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
+        {"image-feature-extraction": FocalNetModel, "image-classification": FocalNetForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/glpn/test_modeling_glpn.py

@@ -146,7 +146,9 @@ class GLPNModelTester:
 class GLPNModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (GLPNModel, GLPNForDepthEstimation) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"depth-estimation": GLPNForDepthEstimation, "feature-extraction": GLPNModel} if is_torch_available() else {}
+        {"depth-estimation": GLPNForDepthEstimation, "image-feature-extraction": GLPNModel}
+        if is_torch_available()
+        else {}
     )
 
     test_head_masking = False

tests/models/imagegpt/test_modeling_imagegpt.py

@@ -271,7 +271,7 @@ class ImageGPTModelTest(ModelTesterMixin, GenerationTesterMixin, PipelineTesterM
     )
     all_generative_model_classes = (ImageGPTForCausalImageModeling,) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": ImageGPTModel, "image-classification": ImageGPTForImageClassification}
+        {"image-feature-extraction": ImageGPTModel, "image-classification": ImageGPTForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/levit/test_modeling_levit.py

@@ -176,7 +176,7 @@ class LevitModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": LevitModel,
+            "image-feature-extraction": LevitModel,
             "image-classification": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
         }
         if is_torch_available()

tests/models/mask2former/test_modeling_mask2former.py

@@ -197,7 +197,7 @@ class Mask2FormerModelTester:
 @require_torch
 class Mask2FormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (Mask2FormerModel, Mask2FormerForUniversalSegmentation) if is_torch_available() else ()
-    pipeline_model_mapping = {"feature-extraction": Mask2FormerModel} if is_torch_available() else {}
+    pipeline_model_mapping = {"image-feature-extraction": Mask2FormerModel} if is_torch_available() else {}
 
     is_encoder_decoder = False
     test_pruning = False

tests/models/maskformer/test_modeling_maskformer.py

@@ -197,7 +197,7 @@ class MaskFormerModelTester:
 class MaskFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
+        {"image-feature-extraction": MaskFormerModel, "image-segmentation": MaskFormerForInstanceSegmentation}
         if is_torch_available()
         else {}
     )

tests/models/mgp_str/test_modeling_mgp_str.py

@@ -31,7 +31,7 @@ if is_torch_available():
     import torch
     from torch import nn
 
-    from transformers import MgpstrForSceneTextRecognition
+    from transformers import MgpstrForSceneTextRecognition, MgpstrModel
 
 
 if is_vision_available():
@@ -118,7 +118,11 @@ class MgpstrModelTester:
 @require_torch
 class MgpstrModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (MgpstrForSceneTextRecognition,) if is_torch_available() else ()
-    pipeline_model_mapping = {"feature-extraction": MgpstrForSceneTextRecognition} if is_torch_available() else {}
+    pipeline_model_mapping = (
+        {"feature-extraction": MgpstrForSceneTextRecognition, "image-feature-extraction": MgpstrModel}
+        if is_torch_available()
+        else {}
+    )
     fx_compatible = False
 
     test_pruning = False

tests/models/mobilenet_v1/test_modeling_mobilenet_v1.py

@@ -147,7 +147,7 @@ class MobileNetV1ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
 
     all_model_classes = (MobileNetV1Model, MobileNetV1ForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": MobileNetV1Model, "image-classification": MobileNetV1ForImageClassification}
+        {"image-feature-extraction": MobileNetV1Model, "image-classification": MobileNetV1ForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/mobilenet_v2/test_modeling_mobilenet_v2.py

@@ -195,7 +195,7 @@ class MobileNetV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": MobileNetV2Model,
+            "image-feature-extraction": MobileNetV2Model,
             "image-classification": MobileNetV2ForImageClassification,
             "image-segmentation": MobileNetV2ForSemanticSegmentation,
         }

tests/models/mobilevit/test_modeling_mobilevit.py

@@ -188,7 +188,7 @@ class MobileViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": MobileViTModel,
+            "image-feature-extraction": MobileViTModel,
             "image-classification": MobileViTForImageClassification,
             "image-segmentation": MobileViTForSemanticSegmentation,
         }

tests/models/mobilevitv2/test_modeling_mobilevitv2.py

@@ -190,7 +190,7 @@ class MobileViTV2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
 
     pipeline_model_mapping = (
         {
-            "feature-extraction": MobileViTV2Model,
+            "image-feature-extraction": MobileViTV2Model,
             "image-classification": MobileViTV2ForImageClassification,
             "image-segmentation": MobileViTV2ForSemanticSegmentation,
         }

tests/models/nat/test_modeling_nat.py

@@ -204,7 +204,7 @@ class NatModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": NatModel, "image-classification": NatForImageClassification}
+        {"image-feature-extraction": NatModel, "image-classification": NatForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/owlv2/test_modeling_owlv2.py

@@ -433,7 +433,10 @@ class Owlv2ModelTester:
 class Owlv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (Owlv2Model,) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": Owlv2Model, "zero-shot-object-detection": Owlv2ForObjectDetection}
+        {
+            "feature-extraction": Owlv2Model,
+            "zero-shot-object-detection": Owlv2ForObjectDetection,
+        }
         if is_torch_available()
         else {}
     )

tests/models/owlvit/test_modeling_owlvit.py

@@ -428,7 +428,10 @@ class OwlViTModelTester:
 class OwlViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (OwlViTModel,) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": OwlViTModel, "zero-shot-object-detection": OwlViTForObjectDetection}
+        {
+            "feature-extraction": OwlViTModel,
+            "zero-shot-object-detection": OwlViTForObjectDetection,
+        }
         if is_torch_available()
         else {}
     )

tests/models/poolformer/test_modeling_poolformer.py

@@ -124,7 +124,7 @@ class PoolFormerModelTester:
 class PoolFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
+        {"image-feature-extraction": PoolFormerModel, "image-classification": PoolFormerForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/pvt/test_modeling_pvt.py

@@ -158,7 +158,7 @@ def prepare_img():
 class PvtModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (PvtModel, PvtForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": PvtModel, "image-classification": PvtForImageClassification}
+        {"image-feature-extraction": PvtModel, "image-classification": PvtForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/regnet/test_modeling_regnet.py

@@ -126,7 +126,7 @@ class RegNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
     all_model_classes = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
+        {"image-feature-extraction": RegNetModel, "image-classification": RegNetForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/resnet/test_modeling_resnet.py

@@ -170,7 +170,7 @@ class ResNetModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
+        {"image-feature-extraction": ResNetModel, "image-classification": ResNetForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/segformer/test_modeling_segformer.py

@@ -171,7 +171,7 @@ class SegformerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
     )
     pipeline_model_mapping = (
         {
-            "feature-extraction": SegformerModel,
+            "image-feature-extraction": SegformerModel,
             "image-classification": SegformerForImageClassification,
             "image-segmentation": SegformerForSemanticSegmentation,
         }

tests/models/swiftformer/test_modeling_swiftformer.py

@@ -139,7 +139,7 @@ class SwiftFormerModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestC
 
     all_model_classes = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
+        {"image-feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/swin/test_modeling_swin.py

@@ -232,7 +232,7 @@ class SwinModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
+        {"image-feature-extraction": SwinModel, "image-classification": SwinForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/swin2sr/test_modeling_swin2sr.py

@@ -162,7 +162,7 @@ class Swin2SRModelTester:
 class Swin2SRModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     all_model_classes = (Swin2SRModel, Swin2SRForImageSuperResolution) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
+        {"image-feature-extraction": Swin2SRModel, "image-to-image": Swin2SRForImageSuperResolution}
         if is_torch_available()
         else {}
     )

tests/models/swinv2/test_modeling_swinv2.py

@@ -217,7 +217,7 @@ class Swinv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": Swinv2Model, "image-classification": Swinv2ForImageClassification}
+        {"image-feature-extraction": Swinv2Model, "image-classification": Swinv2ForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/table_transformer/test_modeling_table_transformer.py

@@ -200,7 +200,7 @@ class TableTransformerModelTest(ModelTesterMixin, GenerationTesterMixin, Pipelin
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": TableTransformerModel, "object-detection": TableTransformerForObjectDetection}
+        {"image-feature-extraction": TableTransformerModel, "object-detection": TableTransformerForObjectDetection}
         if is_torch_available()
         else {}
     )

tests/models/vilt/test_modeling_vilt.py

@@ -228,7 +228,7 @@ class ViltModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": ViltModel, "visual-question-answering": ViltForQuestionAnswering}
+        {"image-feature-extraction": ViltModel, "visual-question-answering": ViltForQuestionAnswering}
         if is_torch_available()
         else {}
     )

tests/models/vit/test_modeling_vit.py

@@ -193,7 +193,7 @@ class ViTModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
         else ()
     )
     pipeline_model_mapping = (
-        {"feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
+        {"image-feature-extraction": ViTModel, "image-classification": ViTForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/vit_hybrid/test_modeling_vit_hybrid.py

@@ -156,7 +156,7 @@ class ViTHybridModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCas
 
     all_model_classes = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
+        {"image-feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/vit_mae/test_modeling_vit_mae.py

@@ -164,7 +164,7 @@ class ViTMAEModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
     """
 
     all_model_classes = (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
-    pipeline_model_mapping = {"feature-extraction": ViTMAEModel} if is_torch_available() else {}
+    pipeline_model_mapping = {"image-feature-extraction": ViTMAEModel} if is_torch_available() else {}
 
     test_pruning = False
     test_torchscript = False

tests/models/vit_msn/test_modeling_vit_msn.py

@@ -152,7 +152,7 @@ class ViTMSNModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
     all_model_classes = (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
+        {"image-feature-extraction": ViTMSNModel, "image-classification": ViTMSNForImageClassification}
         if is_torch_available()
         else {}
     )

tests/models/yolos/test_modeling_yolos.py

@@ -168,7 +168,9 @@ class YolosModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
 
     all_model_classes = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     pipeline_model_mapping = (
-        {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
+        {"image-feature-extraction": YolosModel, "object-detection": YolosForObjectDetection}
+        if is_torch_available()
+        else {}
     )
 
     test_pruning = False

tests/pipelines/test_pipelines_image_feature_extraction.py

@@ -0,0 +1,157 @@
+# Copyright 2024 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import unittest
+
+import numpy as np
+import pytest
+
+from transformers import (
+    MODEL_MAPPING,
+    TF_MODEL_MAPPING,
+    TOKENIZER_MAPPING,
+    ImageFeatureExtractionPipeline,
+    is_tf_available,
+    is_torch_available,
+    is_vision_available,
+    pipeline,
+)
+from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
+
+
+if is_torch_available():
+    import torch
+
+if is_tf_available():
+    import tensorflow as tf
+
+if is_vision_available():
+    from PIL import Image
+
+
+# We will verify our results on an image of cute cats
+def prepare_img():
+    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
+    return image
+
+
+@is_pipeline_test
+class ImageFeatureExtractionPipelineTests(unittest.TestCase):
+    model_mapping = MODEL_MAPPING
+    tf_model_mapping = TF_MODEL_MAPPING
+
+    @require_torch
+    def test_small_model_pt(self):
+        feature_extractor = pipeline(
+            task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
+        )
+        img = prepare_img()
+        outputs = feature_extractor(img)
+        self.assertEqual(
+            nested_simplify(outputs[0][0]),
+            [-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981])  # fmt: skip
+
+    @require_tf
+    def test_small_model_tf(self):
+        feature_extractor = pipeline(
+            task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
+        )
+        img = prepare_img()
+        outputs = feature_extractor(img)
+        self.assertEqual(
+            nested_simplify(outputs[0][0]),
+            [-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981])  # fmt: skip
+
+    @require_torch
+    def test_image_processing_small_model_pt(self):
+        feature_extractor = pipeline(
+            task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
+        )
+
+        # test with image processor parameters
+        image_processor_kwargs = {"size": {"height": 300, "width": 300}}
+        img = prepare_img()
+        with pytest.raises(ValueError):
+            # Image doesn't match model input size
+            feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
+
+        image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
+        img = prepare_img()
+        outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
+        self.assertEqual(np.squeeze(outputs).shape, (226, 32))
+
+    @require_tf
+    def test_image_processing_small_model_tf(self):
+        feature_extractor = pipeline(
+            task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
+        )
+
+        # test with image processor parameters
+        image_processor_kwargs = {"size": {"height": 300, "width": 300}}
+        img = prepare_img()
+        with pytest.raises(ValueError):
+            # Image doesn't match model input size
+            feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
+
+        image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
+        img = prepare_img()
+        outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
+        self.assertEqual(np.squeeze(outputs).shape, (226, 32))
+
+    @require_torch
+    def test_return_tensors_pt(self):
+        feature_extractor = pipeline(
+            task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="pt"
+        )
+        img = prepare_img()
+        outputs = feature_extractor(img, return_tensors=True)
+        self.assertTrue(torch.is_tensor(outputs))
+
+    @require_tf
+    def test_return_tensors_tf(self):
+        feature_extractor = pipeline(
+            task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
+        )
+        img = prepare_img()
+        outputs = feature_extractor(img, return_tensors=True)
+        self.assertTrue(tf.is_tensor(outputs))
+
+    def get_test_pipeline(self, model, tokenizer, processor):
+        if processor is None:
+            self.skipTest("No image processor")
+
+        elif type(model.config) in TOKENIZER_MAPPING:
+            self.skipTest("This is a bimodal model, we need to find a more consistent way to switch on those models.")
+
+        elif model.config.is_encoder_decoder:
+            self.skipTest(
+                """encoder_decoder models are trickier for this pipeline.
+                Do we want encoder + decoder inputs to get some featues?
+                Do we want encoder only features ?
+                For now ignore those.
+                """
+            )
+
+        feature_extractor = ImageFeatureExtractionPipeline(model=model, image_processor=processor)
+        img = prepare_img()
+        return feature_extractor, [img, img]
+
+    def run_pipeline_test(self, feature_extractor, examples):
+        imgs = examples
+        outputs = feature_extractor(imgs[0])
+
+        self.assertEqual(len(outputs), 1)
+
+        outputs = feature_extractor(imgs)
+        self.assertEqual(len(outputs), 2)

tests/test_pipeline_mixin.py

@@ -39,6 +39,7 @@ from .pipelines.test_pipelines_document_question_answering import DocumentQuesti
 from .pipelines.test_pipelines_feature_extraction import FeatureExtractionPipelineTests
 from .pipelines.test_pipelines_fill_mask import FillMaskPipelineTests
 from .pipelines.test_pipelines_image_classification import ImageClassificationPipelineTests
+from .pipelines.test_pipelines_image_feature_extraction import ImageFeatureExtractionPipelineTests
 from .pipelines.test_pipelines_image_segmentation import ImageSegmentationPipelineTests
 from .pipelines.test_pipelines_image_to_image import ImageToImagePipelineTests
 from .pipelines.test_pipelines_image_to_text import ImageToTextPipelineTests
@@ -70,6 +71,7 @@ pipeline_test_mapping = {
     "feature-extraction": {"test": FeatureExtractionPipelineTests},
     "fill-mask": {"test": FillMaskPipelineTests},
     "image-classification": {"test": ImageClassificationPipelineTests},
+    "image-feature-extraction": {"test": ImageFeatureExtractionPipelineTests},
     "image-segmentation": {"test": ImageSegmentationPipelineTests},
     "image-to-image": {"test": ImageToImagePipelineTests},
     "image-to-text": {"test": ImageToTextPipelineTests},
@@ -374,6 +376,13 @@ class PipelineTesterMixin:
     def test_pipeline_image_to_text(self):
         self.run_task_tests(task="image-to-text")
 
+    @is_pipeline_test
+    @require_timm
+    @require_vision
+    @require_torch
+    def test_pipeline_image_feature_extraction(self):
+        self.run_task_tests(task="image-feature-extraction")
+
     @unittest.skip(reason="`run_pipeline_test` is currently not implemented.")
     @is_pipeline_test
     @require_vision

utils/check_docstrings.py

@@ -324,6 +324,7 @@ OBJECTS_TO_IGNORE = [
     "IdeficsConfig",
     "IdeficsProcessor",
     "ImageClassificationPipeline",
+    "ImageFeatureExtractionPipeline",
     "ImageGPTConfig",
     "ImageSegmentationPipeline",
     "ImageToImagePipeline",