Add SiglipForImageClassification and CLIPForImageClassification (#28952)

* First draft

* Add CLIPForImageClassification

* Remove scripts

* Fix doctests

docs/source/en/model_doc/clip.md

@@ -172,6 +172,11 @@ The resource should ideally demonstrate something new instead of duplicating an
 [[autodoc]] CLIPVisionModel
     - forward
 
+## CLIPForImageClassification
+
+[[autodoc]] CLIPForImageClassification
+    - forward
+
 </pt>
 <tf>
 

docs/source/en/model_doc/siglip.md

@@ -140,3 +140,9 @@ If you want to do the pre- and postprocessing yourself, here's how to do that:
 
 [[autodoc]] SiglipVisionModel
     - forward
+
+
+## SiglipForImageClassification
+
+[[autodoc]] SiglipForImageClassification
+    - forward
\ No newline at end of file

docs/source/en/tasks/image_classification.md

@@ -34,7 +34,7 @@ The task illustrated in this tutorial is supported by the following model archit
 
 <!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->
 
-[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [DINOv2](../model_doc/dinov2), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [PVT](../model_doc/pvt), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [SwiftFormer](../model_doc/swiftformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
+[BEiT](../model_doc/beit), [BiT](../model_doc/bit), [CLIP](../model_doc/clip), [ConvNeXT](../model_doc/convnext), [ConvNeXTV2](../model_doc/convnextv2), [CvT](../model_doc/cvt), [Data2VecVision](../model_doc/data2vec-vision), [DeiT](../model_doc/deit), [DiNAT](../model_doc/dinat), [DINOv2](../model_doc/dinov2), [EfficientFormer](../model_doc/efficientformer), [EfficientNet](../model_doc/efficientnet), [FocalNet](../model_doc/focalnet), [ImageGPT](../model_doc/imagegpt), [LeViT](../model_doc/levit), [MobileNetV1](../model_doc/mobilenet_v1), [MobileNetV2](../model_doc/mobilenet_v2), [MobileViT](../model_doc/mobilevit), [MobileViTV2](../model_doc/mobilevitv2), [NAT](../model_doc/nat), [Perceiver](../model_doc/perceiver), [PoolFormer](../model_doc/poolformer), [PVT](../model_doc/pvt), [RegNet](../model_doc/regnet), [ResNet](../model_doc/resnet), [SegFormer](../model_doc/segformer), [SigLIP](../model_doc/siglip), [SwiftFormer](../model_doc/swiftformer), [Swin Transformer](../model_doc/swin), [Swin Transformer V2](../model_doc/swinv2), [VAN](../model_doc/van), [ViT](../model_doc/vit), [ViT Hybrid](../model_doc/vit_hybrid), [ViTMSN](../model_doc/vit_msn)
 
 <!--End of the generated tip-->
 

src/transformers/__init__.py

@@ -1762,6 +1762,7 @@ else:
     _import_structure["models.clip"].extend(
         [
             "CLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "CLIPForImageClassification",
             "CLIPModel",
             "CLIPPreTrainedModel",
             "CLIPTextModel",
@@ -3200,6 +3201,7 @@ else:
     _import_structure["models.siglip"].extend(
         [
             "SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
+            "SiglipForImageClassification",
             "SiglipModel",
             "SiglipPreTrainedModel",
             "SiglipTextModel",
@@ -6447,6 +6449,7 @@ if TYPE_CHECKING:
         )
         from .models.clip import (
             CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            CLIPForImageClassification,
             CLIPModel,
             CLIPPreTrainedModel,
             CLIPTextModel,
@@ -7625,6 +7628,7 @@ if TYPE_CHECKING:
         )
         from .models.siglip import (
             SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            SiglipForImageClassification,
             SiglipModel,
             SiglipPreTrainedModel,
             SiglipTextModel,

src/transformers/models/auto/modeling_auto.py

@@ -498,6 +498,7 @@ MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         # Model for Image Classification mapping
         ("beit", "BeitForImageClassification"),
         ("bit", "BitForImageClassification"),
+        ("clip", "CLIPForImageClassification"),
         ("convnext", "ConvNextForImageClassification"),
         ("convnextv2", "ConvNextV2ForImageClassification"),
         ("cvt", "CvtForImageClassification"),
@@ -540,6 +541,7 @@ MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES = OrderedDict(
         ("regnet", "RegNetForImageClassification"),
         ("resnet", "ResNetForImageClassification"),
         ("segformer", "SegformerForImageClassification"),
+        ("siglip", "SiglipForImageClassification"),
         ("swiftformer", "SwiftFormerForImageClassification"),
         ("swin", "SwinForImageClassification"),
         ("swinv2", "Swinv2ForImageClassification"),

src/transformers/models/clip/__init__.py

@@ -67,6 +67,7 @@ else:
         "CLIPTextModelWithProjection",
         "CLIPVisionModel",
         "CLIPVisionModelWithProjection",
+        "CLIPForImageClassification",
     ]
 
 try:
@@ -136,6 +137,7 @@ if TYPE_CHECKING:
     else:
         from .modeling_clip import (
             CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            CLIPForImageClassification,
             CLIPModel,
             CLIPPreTrainedModel,
             CLIPTextModel,

src/transformers/models/clip/modeling_clip.py

@@ -21,13 +21,15 @@ from typing import Any, Optional, Tuple, Union
 import torch
 import torch.utils.checkpoint
 from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import ACT2FN
 from ...modeling_attn_mask_utils import _create_4d_causal_attention_mask, _prepare_4d_attention_mask
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
     ModelOutput,
+    add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
@@ -38,8 +40,14 @@ from .configuration_clip import CLIPConfig, CLIPTextConfig, CLIPVisionConfig
 
 logger = logging.get_logger(__name__)
 
+# General docstring
+_CONFIG_FOR_DOC = "CLIPConfig"
 _CHECKPOINT_FOR_DOC = "openai/clip-vit-base-patch32"
 
+# Image classification docstring
+_IMAGE_CLASS_CHECKPOINT = "openai/clip-vit-base-patch32"
+_IMAGE_CLASS_EXPECTED_OUTPUT = "LABEL_0"
+
 CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "openai/clip-vit-base-patch32",
     # See all CLIP models at https://huggingface.co/models?filter=clip
@@ -1306,3 +1314,105 @@ class CLIPVisionModelWithProjection(CLIPPreTrainedModel):
             hidden_states=vision_outputs.hidden_states,
             attentions=vision_outputs.attentions,
         )
+
+
+@add_start_docstrings(
+    """
+    CLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of
+    the patch tokens) e.g. for ImageNet.
+    """,
+    CLIP_START_DOCSTRING,
+)
+class CLIPForImageClassification(CLIPPreTrainedModel):
+    main_input_name = "pixel_values"
+
+    def __init__(self, config: CLIPConfig) -> None:
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.vision_model = CLIPVisionTransformer(config.vision_config)
+
+        # Classifier head
+        self.classifier = (
+            nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(CLIP_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_IMAGE_CLASS_CHECKPOINT,
+        output_type=ImageClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
+    )
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple, ImageClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.vision_model(
+            pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        # average pool the patch tokens
+        sequence_output = torch.mean(sequence_output[:, 1:, :], dim=1)
+        # apply classifier
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/models/siglip/__init__.py

@@ -61,6 +61,7 @@ else:
         "SiglipPreTrainedModel",
         "SiglipTextModel",
         "SiglipVisionModel",
+        "SiglipForImageClassification",
     ]
 
 
@@ -97,6 +98,7 @@ if TYPE_CHECKING:
     else:
         from .modeling_siglip import (
             SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
+            SiglipForImageClassification,
             SiglipModel,
             SiglipPreTrainedModel,
             SiglipTextModel,

src/transformers/models/siglip/modeling_siglip.py

@@ -24,14 +24,16 @@ import numpy as np
 import torch
 import torch.utils.checkpoint
 from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 from torch.nn.init import _calculate_fan_in_and_fan_out
 
 from ...activations import ACT2FN
 from ...modeling_attn_mask_utils import _prepare_4d_attention_mask
-from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling
+from ...modeling_outputs import BaseModelOutput, BaseModelOutputWithPooling, ImageClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import (
     ModelOutput,
+    add_code_sample_docstrings,
     add_start_docstrings,
     add_start_docstrings_to_model_forward,
     logging,
@@ -42,8 +44,15 @@ from .configuration_siglip import SiglipConfig, SiglipTextConfig, SiglipVisionCo
 
 logger = logging.get_logger(__name__)
 
+# General docstring
+_CONFIG_FOR_DOC = "SiglipConfig"
 _CHECKPOINT_FOR_DOC = "google/siglip-base-patch16-224"
 
+# Image classification docstring
+_IMAGE_CLASS_CHECKPOINT = "google/siglip-base-patch16-224"
+_IMAGE_CLASS_EXPECTED_OUTPUT = "LABEL_1"
+
+
 SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = [
     "google/siglip-base-patch16-224",
     # See all SigLIP models at https://huggingface.co/models?filter=siglip
@@ -1185,3 +1194,105 @@ class SiglipModel(SiglipPreTrainedModel):
             text_model_output=text_outputs,
             vision_model_output=vision_outputs,
         )
+
+
+@add_start_docstrings(
+    """
+    SigLIP vision encoder with an image classification head on top (a linear layer on top of the pooled final hidden states of
+    the patch tokens) e.g. for ImageNet.
+    """,
+    SIGLIP_START_DOCSTRING,
+)
+class SiglipForImageClassification(SiglipPreTrainedModel):
+    main_input_name = "pixel_values"
+
+    def __init__(self, config: SiglipConfig) -> None:
+        super().__init__(config)
+
+        self.num_labels = config.num_labels
+        self.vision_model = SiglipVisionTransformer(config.vision_config)
+
+        # Classifier head
+        self.classifier = (
+            nn.Linear(config.vision_config.hidden_size, config.num_labels) if config.num_labels > 0 else nn.Identity()
+        )
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    @add_start_docstrings_to_model_forward(SIGLIP_INPUTS_DOCSTRING)
+    @add_code_sample_docstrings(
+        checkpoint=_IMAGE_CLASS_CHECKPOINT,
+        output_type=ImageClassifierOutput,
+        config_class=_CONFIG_FOR_DOC,
+        expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,
+    )
+    def forward(
+        self,
+        pixel_values: Optional[torch.Tensor] = None,
+        labels: Optional[torch.Tensor] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[tuple, ImageClassifierOutput]:
+        r"""
+        labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*):
+            Labels for computing the image classification/regression loss. Indices should be in `[0, ...,
+            config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If
+            `config.num_labels > 1` a classification loss is computed (Cross-Entropy).
+        """
+        output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
+        output_hidden_states = (
+            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
+        )
+        return_dict = return_dict if return_dict is not None else self.config.use_return_dict
+
+        outputs = self.vision_model(
+            pixel_values,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        sequence_output = outputs[0]
+
+        # average pool the patch tokens
+        sequence_output = torch.mean(sequence_output[:, 1:, :], dim=1)
+        # apply classifier
+        logits = self.classifier(sequence_output)
+
+        loss = None
+        if labels is not None:
+            # move labels to correct device to enable model parallelism
+            labels = labels.to(logits.device)
+            if self.config.problem_type is None:
+                if self.num_labels == 1:
+                    self.config.problem_type = "regression"
+                elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
+                    self.config.problem_type = "single_label_classification"
+                else:
+                    self.config.problem_type = "multi_label_classification"
+
+            if self.config.problem_type == "regression":
+                loss_fct = MSELoss()
+                if self.num_labels == 1:
+                    loss = loss_fct(logits.squeeze(), labels.squeeze())
+                else:
+                    loss = loss_fct(logits, labels)
+            elif self.config.problem_type == "single_label_classification":
+                loss_fct = CrossEntropyLoss()
+                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))
+            elif self.config.problem_type == "multi_label_classification":
+                loss_fct = BCEWithLogitsLoss()
+                loss = loss_fct(logits, labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[2:]
+            return ((loss,) + output) if loss is not None else output
+
+        return ImageClassifierOutput(
+            loss=loss,
+            logits=logits,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )

src/transformers/utils/dummy_pt_objects.py

@@ -1901,6 +1901,13 @@ class ClapTextModelWithProjection(metaclass=DummyObject):
 CLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class CLIPForImageClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class CLIPModel(metaclass=DummyObject):
     _backends = ["torch"]
 
@@ -7583,6 +7590,13 @@ class SEWDPreTrainedModel(metaclass=DummyObject):
 SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST = None
 
 
+class SiglipForImageClassification(metaclass=DummyObject):
+    _backends = ["torch"]
+
+    def __init__(self, *args, **kwargs):
+        requires_backends(self, ["torch"])
+
+
 class SiglipModel(metaclass=DummyObject):
     _backends = ["torch"]
 

tests/models/clip/test_modeling_clip.py

@@ -51,6 +51,7 @@ if is_torch_available():
     from torch import nn
 
     from transformers import (
+        CLIPForImageClassification,
         CLIPModel,
         CLIPTextModel,
         CLIPTextModelWithProjection,
@@ -744,6 +745,65 @@ class CLIPModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             self.assertIsNotNone(model)
 
 
+class CLIPForImageClassificationModelTester(CLIPModelTester):
+    def __init__(self, parent):
+        super().__init__(parent)
+        self.batch_size = self.vision_model_tester.batch_size
+        self.num_hidden_layers = self.vision_model_tester.num_hidden_layers
+        self.hidden_size = self.vision_model_tester.hidden_size
+        self.seq_length = self.vision_model_tester.seq_length
+
+    def prepare_config_and_inputs(self):
+        _, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class CLIPForImageClassificationModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (CLIPForImageClassification,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-classification": CLIPForImageClassification} if is_torch_available() else {}
+    fx_compatible = False
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+
+    def setUp(self):
+        self.model_tester = CLIPForImageClassificationModelTester(self)
+
+    @unittest.skip(reason="CLIPForImageClassification does not support inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="CLIPForImageClassification does not support inputs_embeds")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="CLIPForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="CLIPForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="CLIPForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="CLIP uses the same initialization scheme as the Flax original implementation")
+    def test_initialization(self):
+        pass
+
+
 # We will verify our results on an image of cute cats
 def prepare_img():
     url = "http://images.cocodataset.org/val2017/000000039769.jpg"

tests/models/siglip/test_modeling_siglip.py

@@ -12,7 +12,7 @@
 # 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.
-""" Testing suite for the PyTorch Siglip model. """
+""" Testing suite for the PyTorch SigLIP model. """
 
 
 import inspect
@@ -47,7 +47,7 @@ if is_torch_available():
     import torch
     from torch import nn
 
-    from transformers import SiglipModel, SiglipTextModel, SiglipVisionModel
+    from transformers import SiglipForImageClassification, SiglipModel, SiglipTextModel, SiglipVisionModel
     from transformers.models.siglip.modeling_siglip import SIGLIP_PRETRAINED_MODEL_ARCHIVE_LIST
 
 
@@ -584,6 +584,65 @@ class SiglipModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
             self.assertIsNotNone(model)
 
 
+class SiglipForImageClassificationModelTester(SiglipModelTester):
+    def __init__(self, parent):
+        super().__init__(parent)
+        self.batch_size = self.vision_model_tester.batch_size
+        self.num_hidden_layers = self.vision_model_tester.num_hidden_layers
+        self.hidden_size = self.vision_model_tester.hidden_size
+        self.seq_length = self.vision_model_tester.seq_length
+
+    def prepare_config_and_inputs(self):
+        _, pixel_values = self.vision_model_tester.prepare_config_and_inputs()
+        config = self.get_config()
+
+        return config, pixel_values
+
+    def prepare_config_and_inputs_for_common(self):
+        config_and_inputs = self.prepare_config_and_inputs()
+        config, pixel_values = config_and_inputs
+        inputs_dict = {"pixel_values": pixel_values}
+        return config, inputs_dict
+
+
+@require_torch
+class SiglipForImageClassificationModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
+    all_model_classes = (SiglipForImageClassification,) if is_torch_available() else ()
+    pipeline_model_mapping = {"image-classification": SiglipForImageClassification} if is_torch_available() else {}
+    fx_compatible = False
+    test_head_masking = False
+    test_pruning = False
+    test_resize_embeddings = False
+    test_attention_outputs = False
+
+    def setUp(self):
+        self.model_tester = SiglipForImageClassificationModelTester(self)
+
+    @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
+    def test_inputs_embeds(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support inputs_embeds")
+    def test_model_common_attributes(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant(self):
+        pass
+
+    @unittest.skip(reason="SiglipForImageClassification does not support gradient checkpointing yet")
+    def test_training_gradient_checkpointing_use_reentrant_false(self):
+        pass
+
+    @unittest.skip(reason="Siglip uses the same initialization scheme as the Flax original implementation")
+    def test_initialization(self):
+        pass
+
+
 # We will verify our results on an image of cute cats
 def prepare_img():
     url = "http://images.cocodataset.org/val2017/000000039769.jpg"