diff --git a/flash/vision/backbones.py b/flash/vision/backbones.py
index d7a8fb9906..b7c764320a 100644
--- a/flash/vision/backbones.py
+++ b/flash/vision/backbones.py
@@ -18,6 +18,7 @@
 from functools import partial
 from typing import Tuple
 
+import torch
 from pytorch_lightning import LightningModule
 from pytorch_lightning.utilities import _BOLTS_AVAILABLE, rank_zero_warn
 from torch import nn as nn
@@ -180,3 +181,32 @@ def _fn_timm(
         IMAGE_CLASSIFIER_BACKBONES(
             fn=catch_url_error(partial(_fn_timm, model_name)), name=model_name, namespace="vision", package="timm"
         )
+
+
+# Paper: Emerging Properties in Self-Supervised Vision Transformers
+# https://arxiv.org/abs/2104.14294 from Mathilde Caron and al. (29 Apr 2021)
+# weights from https://github.com/facebookresearch/dino
+def dino_deits16(*_, **__):
+    backbone = torch.hub.load('facebookresearch/dino:main', 'dino_deits16')
+    return backbone, 384
+
+
+def dino_deits8(*_, **__):
+    backbone = torch.hub.load('facebookresearch/dino:main', 'dino_deits8')
+    return backbone, 384
+
+
+def dino_vitb16(*_, **__):
+    backbone = torch.hub.load('facebookresearch/dino:main', 'dino_vitb16')
+    return backbone, 768
+
+
+def dino_vitb8(*_, **__):
+    backbone = torch.hub.load('facebookresearch/dino:main', 'dino_vitb8')
+    return backbone, 768
+
+
+IMAGE_CLASSIFIER_BACKBONES(dino_deits16)
+IMAGE_CLASSIFIER_BACKBONES(dino_deits8)
+IMAGE_CLASSIFIER_BACKBONES(dino_vitb16)
+IMAGE_CLASSIFIER_BACKBONES(dino_vitb8)
diff --git a/flash/vision/classification/model.py b/flash/vision/classification/model.py
index f11a50eb01..2d68e07341 100644
--- a/flash/vision/classification/model.py
+++ b/flash/vision/classification/model.py
@@ -105,7 +105,6 @@ def __init__(
 
         head = head(num_features, num_classes) if isinstance(head, FunctionType) else head
         self.head = head or nn.Sequential(
-            nn.AdaptiveAvgPool2d((1, 1)),
             nn.Flatten(),
             nn.Linear(num_features, num_classes),
         )
diff --git a/flash_examples/finetuning/image_classification.py b/flash_examples/finetuning/image_classification.py
index 4a93ec1785..eb863f4e9f 100644
--- a/flash_examples/finetuning/image_classification.py
+++ b/flash_examples/finetuning/image_classification.py
@@ -48,7 +48,7 @@ def fn_resnet(pretrained: bool = True):
 print(ImageClassifier.available_backbones())
 
 # 4. Build the model
-model = ImageClassifier(backbone="resnet18", num_classes=datamodule.num_classes)
+model = ImageClassifier(backbone="dino_vitb16", num_classes=datamodule.num_classes)
 
 # 5. Create the trainer.
 trainer = flash.Trainer(max_epochs=1, limit_train_batches=1, limit_val_batches=1)