diff --git a/CHANGELOG.md b/CHANGELOG.md
index f4c07de9ad6..cfb24ba1ccb 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -35,6 +35,21 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
   * `PSNR`
 
 
+- Removed deprecated functions, and warnings in classification and regression ([#806](https://github.com/PyTorchLightning/metrics/pull/806))
+  * `FBeta`
+  * `F1`
+  * `Hinge`
+  * `IoU`
+  * `functional.iou`
+  * `MatthewsCorrcoef`
+  * `PearsonCorrcoef`
+  * `SpearmanCorrcoef`
+  * `functional.fbeta`
+  * `functional.f1`
+  * `functional.hinge`
+
+
+
 - Removed deprecated functions, and warnings in detection and pairwise ([#804](https://github.com/PyTorchLightning/metrics/pull/804))
   * `MAP`
   * `functional.pairwise.manhatten`
diff --git a/torchmetrics/__init__.py b/torchmetrics/__init__.py
index d522762d78c..d3990fb2cf7 100644
--- a/torchmetrics/__init__.py
+++ b/torchmetrics/__init__.py
@@ -20,10 +20,9 @@
     SignalDistortionRatio,
     SignalNoiseRatio,
 )
-from torchmetrics.classification import (  # noqa: E402, F401
+from torchmetrics.classification import (  # noqa: E402
     AUC,
     AUROC,
-    F1,
     ROC,
     Accuracy,
     AveragePrecision,
@@ -34,15 +33,11 @@
     CohenKappa,
     ConfusionMatrix,
     F1Score,
-    FBeta,
     FBetaScore,
     HammingDistance,
-    Hinge,
     HingeLoss,
-    IoU,
     JaccardIndex,
     KLDivergence,
-    MatthewsCorrcoef,
     MatthewsCorrCoef,
     Precision,
     PrecisionRecallCurve,
@@ -64,10 +59,8 @@
     MeanAbsolutePercentageError,
     MeanSquaredError,
     MeanSquaredLogError,
-    PearsonCorrcoef,
     PearsonCorrCoef,
     R2Score,
-    SpearmanCorrcoef,
     SpearmanCorrCoef,
     SymmetricMeanAbsolutePercentageError,
     TweedieDevianceScore,
@@ -117,16 +110,12 @@
     "TweedieDevianceScore",
     "ExplainedVariance",
     "ExtendedEditDistance",
-    "F1",
     "F1Score",
-    "FBeta",
     "FBetaScore",
     "HammingDistance",
-    "Hinge",
     "HingeLoss",
     "JaccardIndex",
     "KLDivergence",
-    "MatthewsCorrcoef",
     "MatthewsCorrCoef",
     "MaxMetric",
     "MeanAbsoluteError",
@@ -141,7 +130,6 @@
     "MinMetric",
     "MultioutputWrapper",
     "MultiScaleStructuralSimilarityIndexMeasure",
-    "PearsonCorrcoef",
     "PearsonCorrCoef",
     "PermutationInvariantTraining",
     "Precision",
@@ -163,7 +151,6 @@
     "ScaleInvariantSignalDistortionRatio",
     "ScaleInvariantSignalNoiseRatio",
     "SignalNoiseRatio",
-    "SpearmanCorrcoef",
     "SpearmanCorrCoef",
     "Specificity",
     "SQuAD",
diff --git a/torchmetrics/classification/__init__.py b/torchmetrics/classification/__init__.py
index 734b37153c6..996d61e5a46 100644
--- a/torchmetrics/classification/__init__.py
+++ b/torchmetrics/classification/__init__.py
@@ -21,13 +21,12 @@
 from torchmetrics.classification.calibration_error import CalibrationError  # noqa: F401
 from torchmetrics.classification.cohen_kappa import CohenKappa  # noqa: F401
 from torchmetrics.classification.confusion_matrix import ConfusionMatrix  # noqa: F401
-from torchmetrics.classification.f_beta import F1, F1Score, FBeta, FBetaScore  # noqa: F401
+from torchmetrics.classification.f_beta import F1Score, FBetaScore  # noqa: F401
 from torchmetrics.classification.hamming import HammingDistance  # noqa: F401
-from torchmetrics.classification.hinge import Hinge, HingeLoss  # noqa: F401
-from torchmetrics.classification.iou import IoU  # noqa: F401
+from torchmetrics.classification.hinge import HingeLoss  # noqa: F401
 from torchmetrics.classification.jaccard import JaccardIndex  # noqa: F401
 from torchmetrics.classification.kl_divergence import KLDivergence  # noqa: F401
-from torchmetrics.classification.matthews_corrcoef import MatthewsCorrCoef, MatthewsCorrcoef  # noqa: F401
+from torchmetrics.classification.matthews_corrcoef import MatthewsCorrCoef  # noqa: F401
 from torchmetrics.classification.precision_recall import Precision, Recall  # noqa: F401
 from torchmetrics.classification.precision_recall_curve import PrecisionRecallCurve  # noqa: F401
 from torchmetrics.classification.roc import ROC  # noqa: F401
diff --git a/torchmetrics/classification/f_beta.py b/torchmetrics/classification/f_beta.py
index 0a716964453..24ad3bc47ca 100644
--- a/torchmetrics/classification/f_beta.py
+++ b/torchmetrics/classification/f_beta.py
@@ -14,12 +14,10 @@
 from typing import Any, Callable, Optional
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 
 from torchmetrics.classification.stat_scores import StatScores
 from torchmetrics.functional.classification.f_beta import _fbeta_compute
-from torchmetrics.utilities import _future_warning
 from torchmetrics.utilities.enums import AverageMethod
 
 
@@ -176,51 +174,6 @@ def compute(self) -> Tensor:
         return _fbeta_compute(tp, fp, tn, fn, self.beta, self.ignore_index, self.average, self.mdmc_reduce)
 
 
-class FBeta(FBetaScore):
-    r"""
-    Computes `F-score`_, specifically:
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.FBetaScore`. Will be removed in v0.8.
-
-    Example::
-        >>> f_beta = FBetaScore(num_classes=3, beta=0.5)
-        >>> f_beta(torch.tensor([0, 2, 1, 0, 0, 1]), torch.tensor([0, 1, 2, 0, 1, 2]))
-        tensor(0.3333)
-    """
-
-    @deprecated(target=FBetaScore, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        num_classes: Optional[int] = None,
-        beta: float = 1.0,
-        threshold: float = 0.5,
-        average: str = "micro",
-        mdmc_average: Optional[str] = None,
-        ignore_index: Optional[int] = None,
-        top_k: Optional[int] = None,
-        multiclass: Optional[bool] = None,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable = None,
-    ) -> None:
-        void(
-            num_classes,
-            beta,
-            threshold,
-            average,
-            mdmc_average,
-            ignore_index,
-            top_k,
-            multiclass,
-            compute_on_step,
-            dist_sync_on_step,
-            process_group,
-            dist_sync_fn,
-        )
-
-
 class F1Score(FBetaScore):
     """Computes F1 metric. F1 metrics correspond to a harmonic mean of the precision and recall scores.
 
@@ -347,48 +300,3 @@ def __init__(
             process_group=process_group,
             dist_sync_fn=dist_sync_fn,
         )
-
-
-class F1(F1Score):
-    """Computes F1 metric. F1 metrics correspond to a harmonic mean of the precision and recall scores.
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.F1Score`. Will be removed in v0.8.
-
-    Example:
-        >>> from torchmetrics import F1
-        >>> target = torch.tensor([0, 1, 2, 0, 1, 2])
-        >>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
-        >>> f1 = F1(num_classes=3)
-        >>> f1(preds, target)
-        tensor(0.3333)
-    """
-
-    @deprecated(target=F1Score, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        num_classes: Optional[int] = None,
-        threshold: float = 0.5,
-        average: str = "micro",
-        mdmc_average: Optional[str] = None,
-        ignore_index: Optional[int] = None,
-        top_k: Optional[int] = None,
-        multiclass: Optional[bool] = None,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable = None,
-    ) -> None:
-        void(
-            num_classes,
-            threshold,
-            average,
-            mdmc_average,
-            ignore_index,
-            top_k,
-            multiclass,
-            compute_on_step,
-            dist_sync_on_step,
-            process_group,
-            dist_sync_fn,
-        )
diff --git a/torchmetrics/classification/hinge.py b/torchmetrics/classification/hinge.py
index 04b4736cb22..ac69491d97e 100644
--- a/torchmetrics/classification/hinge.py
+++ b/torchmetrics/classification/hinge.py
@@ -13,12 +13,10 @@
 # limitations under the License.
 from typing import Any, Callable, Optional, Union
 
-from deprecate import deprecated, void
 from torch import Tensor, tensor
 
 from torchmetrics.functional.classification.hinge import MulticlassMode, _hinge_compute, _hinge_update
 from torchmetrics.metric import Metric
-from torchmetrics.utilities import _future_warning
 
 
 class HingeLoss(Metric):
@@ -128,41 +126,3 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
 
     def compute(self) -> Tensor:
         return _hinge_compute(self.measure, self.total)
-
-
-class Hinge(HingeLoss):
-    r"""
-    Computes the mean `Hinge loss`_, typically used for Support Vector Machines (SVMs).
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.HingeLoss`. Will be removed in v0.8.
-
-    Example (binary case):
-        >>> import torch
-        >>> hinge = Hinge()
-        >>> hinge(torch.tensor([-2.2, 2.4, 0.1]), torch.tensor([0, 1, 1]))
-        tensor(0.3000)
-
-    Example (default / multiclass case):
-        >>> hinge = Hinge()
-        >>> hinge(torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]]), torch.tensor([0, 1, 2]))
-        tensor(2.9000)
-
-    Example (multiclass example, one vs all mode):
-        >>> hinge = Hinge(multiclass_mode="one-vs-all")
-        >>> hinge(torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]]), torch.tensor([0, 1, 2]))
-        tensor([2.2333, 1.5000, 1.2333])
-
-    """
-
-    @deprecated(target=HingeLoss, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        squared: bool = False,
-        multiclass_mode: Optional[Union[str, MulticlassMode]] = None,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable = None,
-    ) -> None:
-        void(squared, multiclass_mode, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
diff --git a/torchmetrics/classification/iou.py b/torchmetrics/classification/iou.py
deleted file mode 100644
index d11a9afab83..00000000000
--- a/torchmetrics/classification/iou.py
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright The PyTorch Lightning team.
-#
-# 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 Any, Optional
-
-import torch
-from deprecate import deprecated, void
-
-from torchmetrics.classification.jaccard import JaccardIndex
-from torchmetrics.utilities import _future_warning
-
-
-class IoU(JaccardIndex):
-    r"""
-    Computes Intersection over union, or `Jaccard index`_:
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.JaccardIndex`. Will be removed in v0.8.
-
-    Example:
-        >>> from torchmetrics import IoU
-        >>> target = torch.randint(0, 2, (10, 25, 25))
-        >>> pred = torch.tensor(target)
-        >>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
-        >>> iou = IoU(num_classes=2)
-        >>> iou(pred, target)
-        tensor(0.9660)
-
-    """
-
-    @deprecated(target=JaccardIndex, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        num_classes: int,
-        ignore_index: Optional[int] = None,
-        absent_score: float = 0.0,
-        threshold: float = 0.5,
-        reduction: str = "elementwise_mean",
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-    ) -> None:
-        void(
-            num_classes,
-            ignore_index,
-            absent_score,
-            threshold,
-            reduction,
-            compute_on_step,
-            dist_sync_on_step,
-            process_group,
-        )
diff --git a/torchmetrics/classification/matthews_corrcoef.py b/torchmetrics/classification/matthews_corrcoef.py
index ffe96767666..c643927f314 100644
--- a/torchmetrics/classification/matthews_corrcoef.py
+++ b/torchmetrics/classification/matthews_corrcoef.py
@@ -14,7 +14,6 @@
 from typing import Any, Callable, Optional
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 
 from torchmetrics.functional.classification.matthews_corrcoef import (
@@ -22,7 +21,6 @@
     _matthews_corrcoef_update,
 )
 from torchmetrics.metric import Metric
-from torchmetrics.utilities import _future_warning
 
 
 class MatthewsCorrCoef(Metric):
@@ -112,29 +110,3 @@ def update(self, preds: Tensor, target: Tensor) -> None:  # type: ignore
     def compute(self) -> Tensor:
         """Computes matthews correlation coefficient."""
         return _matthews_corrcoef_compute(self.confmat)
-
-
-class MatthewsCorrcoef(MatthewsCorrCoef):
-    """Calculates `Matthews correlation coefficient`_ that measures the general correlation or quality of a
-    classification.
-
-    Example:
-        >>> matthews_corrcoef = MatthewsCorrcoef(num_classes=2)
-        >>> matthews_corrcoef(torch.tensor([0, 1, 0, 0]), torch.tensor([1, 1, 0, 0]))
-        tensor(0.5774)
-
-    .. deprecated:: v0.7
-        Renamed in favor of :class:`torchmetrics.MatthewsCorrCoef`. Will be removed in v0.8.
-    """
-
-    @deprecated(target=MatthewsCorrCoef, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        num_classes: int,
-        threshold: float = 0.5,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Callable = None,
-    ) -> None:
-        void(num_classes, threshold, compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)
diff --git a/torchmetrics/functional/__init__.py b/torchmetrics/functional/__init__.py
index 25fa04ba54b..e4fde19deb7 100644
--- a/torchmetrics/functional/__init__.py
+++ b/torchmetrics/functional/__init__.py
@@ -22,10 +22,9 @@
 from torchmetrics.functional.classification.cohen_kappa import cohen_kappa
 from torchmetrics.functional.classification.confusion_matrix import confusion_matrix
 from torchmetrics.functional.classification.dice import dice_score
-from torchmetrics.functional.classification.f_beta import f1, f1_score, fbeta, fbeta_score
+from torchmetrics.functional.classification.f_beta import f1_score, fbeta_score
 from torchmetrics.functional.classification.hamming import hamming_distance
-from torchmetrics.functional.classification.hinge import hinge, hinge_loss
-from torchmetrics.functional.classification.iou import iou  # noqa: F401
+from torchmetrics.functional.classification.hinge import hinge_loss
 from torchmetrics.functional.classification.jaccard import jaccard_index
 from torchmetrics.functional.classification.kl_divergence import kl_divergence
 from torchmetrics.functional.classification.matthews_corrcoef import matthews_corrcoef
@@ -95,12 +94,9 @@
     "dice_score",
     "explained_variance",
     "extended_edit_distance",
-    "f1",
     "f1_score",
-    "fbeta",
     "fbeta_score",
     "hamming_distance",
-    "hinge",
     "hinge_loss",
     "image_gradients",
     "jaccard_index",
diff --git a/torchmetrics/functional/classification/__init__.py b/torchmetrics/functional/classification/__init__.py
index 70743f93e86..513c59fb842 100644
--- a/torchmetrics/functional/classification/__init__.py
+++ b/torchmetrics/functional/classification/__init__.py
@@ -19,9 +19,9 @@
 from torchmetrics.functional.classification.cohen_kappa import cohen_kappa  # noqa: F401
 from torchmetrics.functional.classification.confusion_matrix import confusion_matrix  # noqa: F401
 from torchmetrics.functional.classification.dice import dice_score  # noqa: F401
-from torchmetrics.functional.classification.f_beta import f1, f1_score, fbeta, fbeta_score  # noqa: F401
+from torchmetrics.functional.classification.f_beta import f1_score, fbeta_score  # noqa: F401
 from torchmetrics.functional.classification.hamming import hamming_distance  # noqa: F401
-from torchmetrics.functional.classification.hinge import hinge, hinge_loss  # noqa: F401
+from torchmetrics.functional.classification.hinge import hinge_loss  # noqa: F401
 from torchmetrics.functional.classification.jaccard import jaccard_index  # noqa: F401
 from torchmetrics.functional.classification.kl_divergence import kl_divergence  # noqa: F401
 from torchmetrics.functional.classification.matthews_corrcoef import matthews_corrcoef  # noqa: F401
diff --git a/torchmetrics/functional/classification/f_beta.py b/torchmetrics/functional/classification/f_beta.py
index 60571603b8a..f6273e9f982 100644
--- a/torchmetrics/functional/classification/f_beta.py
+++ b/torchmetrics/functional/classification/f_beta.py
@@ -14,11 +14,9 @@
 from typing import Optional
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 
 from torchmetrics.functional.classification.stat_scores import _reduce_stat_scores, _stat_scores_update
-from torchmetrics.utilities import _future_warning
 from torchmetrics.utilities.enums import AverageMethod as AvgMethod
 from torchmetrics.utilities.enums import MDMCAverageMethod
 
@@ -245,32 +243,6 @@ def fbeta_score(
     return _fbeta_compute(tp, fp, tn, fn, beta, ignore_index, average, mdmc_average)
 
 
-@deprecated(target=fbeta_score, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-def fbeta(
-    preds: Tensor,
-    target: Tensor,
-    beta: float = 1.0,
-    average: str = "micro",
-    mdmc_average: Optional[str] = None,
-    ignore_index: Optional[int] = None,
-    num_classes: Optional[int] = None,
-    threshold: float = 0.5,
-    top_k: Optional[int] = None,
-    multiclass: Optional[bool] = None,
-) -> Tensor:
-    r"""
-    Computes f_beta metric.
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.functional.f1_score`. Will be removed in v0.8.
-
-    Example::
-        >>> fbeta(torch.tensor([0, 2, 1, 0, 0, 1]), torch.tensor([0, 1, 2, 0, 1, 2]), num_classes=3, beta=0.5)
-        tensor(0.3333)
-    """
-    return void(preds, target, beta, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass)
-
-
 def f1_score(
     preds: Tensor,
     target: Tensor,
@@ -379,31 +351,3 @@ def f1_score(
     return fbeta_score(
         preds, target, 1.0, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass
     )
-
-
-@deprecated(target=f1_score, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-def f1(
-    preds: Tensor,
-    target: Tensor,
-    beta: float = 1.0,
-    average: str = "micro",
-    mdmc_average: Optional[str] = None,
-    ignore_index: Optional[int] = None,
-    num_classes: Optional[int] = None,
-    threshold: float = 0.5,
-    top_k: Optional[int] = None,
-    multiclass: Optional[bool] = None,
-) -> Tensor:
-    """Computes F1 metric. F1 metrics correspond to a equally weighted average of the precision and recall scores.
-
-    .. deprecated:: v0.7
-        Use :class:`torchmetrics.functional.f1_score`. Will be removed in v0.8.
-
-    Example:
-        >>> from torchmetrics.functional import f1
-        >>> target = torch.tensor([0, 1, 2, 0, 1, 2])
-        >>> preds = torch.tensor([0, 2, 1, 0, 0, 1])
-        >>> f1(preds, target, num_classes=3)
-        tensor(0.3333)
-    """
-    return void(preds, target, beta, average, mdmc_average, ignore_index, num_classes, threshold, top_k, multiclass)
diff --git a/torchmetrics/functional/classification/hinge.py b/torchmetrics/functional/classification/hinge.py
index 649d3c8a5d6..e1a3320bc18 100644
--- a/torchmetrics/functional/classification/hinge.py
+++ b/torchmetrics/functional/classification/hinge.py
@@ -14,10 +14,8 @@
 from typing import Optional, Tuple, Union
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor, tensor
 
-from torchmetrics.utilities import _future_warning
 from torchmetrics.utilities.checks import _input_squeeze
 from torchmetrics.utilities.data import to_onehot
 from torchmetrics.utilities.enums import DataType, EnumStr
@@ -231,34 +229,3 @@ def hinge_loss(
     """
     measure, total = _hinge_update(preds, target, squared=squared, multiclass_mode=multiclass_mode)
     return _hinge_compute(measure, total)
-
-
-@deprecated(target=hinge_loss, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-def hinge(
-    preds: Tensor,
-    target: Tensor,
-    squared: bool = False,
-    multiclass_mode: Optional[Union[str, MulticlassMode]] = None,
-) -> Tensor:
-    r"""
-    Computes the mean `Hinge loss`_ typically used for Support Vector Machines (SVMs).
-
-    .. deprecated:: v0.7
-        Use :func:`torchmetrics.functional.hinge_loss`. Will be removed in v0.8.
-
-    Example (binary case):
-        >>> import torch
-        >>> hinge(torch.tensor([-2.2, 2.4, 0.1]), torch.tensor([0, 1, 1]))
-        tensor(0.3000)
-
-    Example (default / multiclass case):
-        >>> hinge(torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]]), torch.tensor([0, 1, 2]))
-        tensor(2.9000)
-
-    Example (multiclass example, one vs all mode):
-        >>> target = torch.tensor([0, 1, 2])
-        >>> preds = torch.tensor([[-1.0, 0.9, 0.2], [0.5, -1.1, 0.8], [2.2, -0.5, 0.3]])
-        >>> hinge(preds, target, multiclass_mode="one-vs-all")
-        tensor([2.2333, 1.5000, 1.2333])
-    """
-    return void(preds, target, squared, multiclass_mode)
diff --git a/torchmetrics/functional/classification/iou.py b/torchmetrics/functional/classification/iou.py
deleted file mode 100644
index cba06f0d46d..00000000000
--- a/torchmetrics/functional/classification/iou.py
+++ /dev/null
@@ -1,48 +0,0 @@
-# Copyright The PyTorch Lightning team.
-#
-# 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 Optional
-
-import torch
-from deprecate import deprecated, void
-from torch import Tensor
-
-from torchmetrics.functional.classification.jaccard import jaccard_index
-from torchmetrics.utilities import _future_warning
-
-
-@deprecated(target=jaccard_index, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-def iou(
-    preds: Tensor,
-    target: Tensor,
-    ignore_index: Optional[int] = None,
-    absent_score: float = 0.0,
-    threshold: float = 0.5,
-    num_classes: Optional[int] = None,
-    reduction: str = "elementwise_mean",
-) -> Tensor:
-    r"""
-     Computes `Jaccard index`_
-
-    .. deprecated:: v0.7
-         Use :func:`torchmetrics.functional.jaccard_index`. Will be removed in v0.8.
-
-     Example:
-         >>> from torchmetrics.functional import iou
-         >>> target = torch.randint(0, 2, (10, 25, 25))
-         >>> pred = torch.tensor(target)
-         >>> pred[2:5, 7:13, 9:15] = 1 - pred[2:5, 7:13, 9:15]
-         >>> iou(pred, target)
-         tensor(0.9660)
-    """
-    return void(preds, target, ignore_index, absent_score, threshold, num_classes, reduction)
diff --git a/torchmetrics/regression/__init__.py b/torchmetrics/regression/__init__.py
index 6522cb59ae3..0ea11d19dfc 100644
--- a/torchmetrics/regression/__init__.py
+++ b/torchmetrics/regression/__init__.py
@@ -17,8 +17,8 @@
 from torchmetrics.regression.mae import MeanAbsoluteError  # noqa: F401
 from torchmetrics.regression.mape import MeanAbsolutePercentageError  # noqa: F401
 from torchmetrics.regression.mse import MeanSquaredError  # noqa: F401
-from torchmetrics.regression.pearson import PearsonCorrCoef, PearsonCorrcoef  # noqa: F401
+from torchmetrics.regression.pearson import PearsonCorrCoef  # noqa: F401
 from torchmetrics.regression.r2 import R2Score  # noqa: F401
-from torchmetrics.regression.spearman import SpearmanCorrCoef, SpearmanCorrcoef  # noqa: F401
+from torchmetrics.regression.spearman import SpearmanCorrCoef  # noqa: F401
 from torchmetrics.regression.symmetric_mape import SymmetricMeanAbsolutePercentageError  # noqa: F401
 from torchmetrics.regression.tweedie_deviance import TweedieDevianceScore  # noqa: F401
diff --git a/torchmetrics/regression/pearson.py b/torchmetrics/regression/pearson.py
index d4a19259c1d..8224a2e112c 100644
--- a/torchmetrics/regression/pearson.py
+++ b/torchmetrics/regression/pearson.py
@@ -14,12 +14,10 @@
 from typing import Any, List, Optional, Tuple
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 
 from torchmetrics.functional.regression.pearson import _pearson_corrcoef_compute, _pearson_corrcoef_update
 from torchmetrics.metric import Metric
-from torchmetrics.utilities import _future_warning
 
 
 def _final_aggregation(
@@ -141,27 +139,3 @@ def compute(self) -> Tensor:
             n_total = self.n_total
 
         return _pearson_corrcoef_compute(var_x, var_y, corr_xy, n_total)
-
-
-class PearsonCorrcoef(PearsonCorrCoef):
-    r"""
-    Computes `Pearson Correlation Coefficient`_:
-
-    Example:
-        >>> pearson = PearsonCorrcoef()
-        >>> pearson(torch.tensor([2.5, 0.0, 2, 8]), torch.tensor([3, -0.5, 2, 7]))
-        tensor(0.9849)
-
-    .. deprecated:: v0.7
-        Renamed in favor of :class:`torchmetrics.PearsonCorrCoef`. Will be removed in v0.8.
-
-    """
-
-    @deprecated(target=PearsonCorrCoef, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-    ) -> None:
-        void(compute_on_step, dist_sync_on_step, process_group)
diff --git a/torchmetrics/regression/spearman.py b/torchmetrics/regression/spearman.py
index 549ce77c62f..5e1ce05529c 100644
--- a/torchmetrics/regression/spearman.py
+++ b/torchmetrics/regression/spearman.py
@@ -14,12 +14,11 @@
 from typing import Any, Callable, List, Optional
 
 import torch
-from deprecate import deprecated, void
 from torch import Tensor
 
 from torchmetrics.functional.regression.spearman import _spearman_corrcoef_compute, _spearman_corrcoef_update
 from torchmetrics.metric import Metric
-from torchmetrics.utilities import _future_warning, rank_zero_warn
+from torchmetrics.utilities import rank_zero_warn
 from torchmetrics.utilities.data import dim_zero_cat
 
 
@@ -96,26 +95,3 @@ def compute(self) -> Tensor:
         preds = dim_zero_cat(self.preds)
         target = dim_zero_cat(self.target)
         return _spearman_corrcoef_compute(preds, target)
-
-
-class SpearmanCorrcoef(SpearmanCorrCoef):
-    """Computes `spearmans rank correlation coefficient`_.
-
-    Example:
-        >>> spearman = SpearmanCorrCoef()
-        >>> spearman(torch.tensor([2.5, 0.0, 2, 8]), torch.tensor([3, -0.5, 2, 7]))
-        tensor(1.0000)
-
-    .. deprecated:: v0.7
-        Renamed in favor of :class:`torchmetrics.SpearmanCorrCoef`. Will be removed in v0.8.
-    """
-
-    @deprecated(target=SpearmanCorrCoef, deprecated_in="0.7", remove_in="0.8", stream=_future_warning)
-    def __init__(
-        self,
-        compute_on_step: bool = True,
-        dist_sync_on_step: bool = False,
-        process_group: Optional[Any] = None,
-        dist_sync_fn: Optional[Callable] = None,
-    ) -> None:
-        void(compute_on_step, dist_sync_on_step, process_group, dist_sync_fn)