diff --git a/docs/source/models.rst b/docs/source/models.rst
index 50af05360e4..39543cb8027 100644
--- a/docs/source/models.rst
+++ b/docs/source/models.rst
@@ -98,58 +98,6 @@ You can construct a model with random weights by calling its constructor:
     convnext_large = models.convnext_large()
 
 We provide pre-trained models, using the PyTorch :mod:`torch.utils.model_zoo`.
-These can be constructed by passing ``pretrained=True``:
-
-.. code:: python
-
-    import torchvision.models as models
-    resnet18 = models.resnet18(pretrained=True)
-    alexnet = models.alexnet(pretrained=True)
-    squeezenet = models.squeezenet1_0(pretrained=True)
-    vgg16 = models.vgg16(pretrained=True)
-    densenet = models.densenet161(pretrained=True)
-    inception = models.inception_v3(pretrained=True)
-    googlenet = models.googlenet(pretrained=True)
-    shufflenet = models.shufflenet_v2_x1_0(pretrained=True)
-    mobilenet_v2 = models.mobilenet_v2(pretrained=True)
-    mobilenet_v3_large = models.mobilenet_v3_large(pretrained=True)
-    mobilenet_v3_small = models.mobilenet_v3_small(pretrained=True)
-    resnext50_32x4d = models.resnext50_32x4d(pretrained=True)
-    wide_resnet50_2 = models.wide_resnet50_2(pretrained=True)
-    mnasnet = models.mnasnet1_0(pretrained=True)
-    efficientnet_b0 = models.efficientnet_b0(pretrained=True)
-    efficientnet_b1 = models.efficientnet_b1(pretrained=True)
-    efficientnet_b2 = models.efficientnet_b2(pretrained=True)
-    efficientnet_b3 = models.efficientnet_b3(pretrained=True)
-    efficientnet_b4 = models.efficientnet_b4(pretrained=True)
-    efficientnet_b5 = models.efficientnet_b5(pretrained=True)
-    efficientnet_b6 = models.efficientnet_b6(pretrained=True)
-    efficientnet_b7 = models.efficientnet_b7(pretrained=True)
-    efficientnet_v2_s = models.efficientnet_v2_s(pretrained=True)
-    efficientnet_v2_m = models.efficientnet_v2_m(pretrained=True)
-    efficientnet_v2_l = models.efficientnet_v2_l(pretrained=True)
-    regnet_y_400mf = models.regnet_y_400mf(pretrained=True)
-    regnet_y_800mf = models.regnet_y_800mf(pretrained=True)
-    regnet_y_1_6gf = models.regnet_y_1_6gf(pretrained=True)
-    regnet_y_3_2gf = models.regnet_y_3_2gf(pretrained=True)
-    regnet_y_8gf = models.regnet_y_8gf(pretrained=True)
-    regnet_y_16gf = models.regnet_y_16gf(pretrained=True)
-    regnet_y_32gf = models.regnet_y_32gf(pretrained=True)
-    regnet_x_400mf = models.regnet_x_400mf(pretrained=True)
-    regnet_x_800mf = models.regnet_x_800mf(pretrained=True)
-    regnet_x_1_6gf = models.regnet_x_1_6gf(pretrained=True)
-    regnet_x_3_2gf = models.regnet_x_3_2gf(pretrained=True)
-    regnet_x_8gf = models.regnet_x_8gf(pretrained=True)
-    regnet_x_16gf = models.regnet_x_16gf(pretrainedTrue)
-    regnet_x_32gf = models.regnet_x_32gf(pretrained=True)
-    vit_b_16 = models.vit_b_16(pretrained=True)
-    vit_b_32 = models.vit_b_32(pretrained=True)
-    vit_l_16 = models.vit_l_16(pretrained=True)
-    vit_l_32 = models.vit_l_32(pretrained=True)
-    convnext_tiny = models.convnext_tiny(pretrained=True)
-    convnext_small = models.convnext_small(pretrained=True)
-    convnext_base = models.convnext_base(pretrained=True)
-    convnext_large = models.convnext_large(pretrained=True)
 
 Instancing a pre-trained model will download its weights to a cache directory.
 This directory can be set using the `TORCH_HOME` environment variable. See
@@ -525,7 +473,7 @@ Obtaining a pre-trained quantized model can be done with a few lines of code:
 .. code:: python
 
     import torchvision.models as models
-    model = models.quantization.mobilenet_v2(pretrained=True, quantize=True)
+    model = models.quantization.mobilenet_v2(weights=MobileNet_V2_QuantizedWeights.IMAGENET1K_QNNPACK_V1, quantize=True)
     model.eval()
     # run the model with quantized inputs and weights
     out = model(torch.rand(1, 3, 224, 224))
diff --git a/gallery/plot_optical_flow.py b/gallery/plot_optical_flow.py
index 770610fb971..5149ebc541b 100644
--- a/gallery/plot_optical_flow.py
+++ b/gallery/plot_optical_flow.py
@@ -96,7 +96,7 @@ def plot(imgs, **imshow_kwargs):
 def preprocess(img1_batch, img2_batch):
     img1_batch = F.resize(img1_batch, size=[520, 960])
     img2_batch = F.resize(img2_batch, size=[520, 960])
-    return transforms(img1_batch, img2_batch)[:2]
+    return transforms(img1_batch, img2_batch)
 
 
 img1_batch, img2_batch = preprocess(img1_batch, img2_batch)
diff --git a/gallery/plot_repurposing_annotations.py b/gallery/plot_repurposing_annotations.py
index a826a2523f2..7bb68617a17 100644
--- a/gallery/plot_repurposing_annotations.py
+++ b/gallery/plot_repurposing_annotations.py
@@ -146,7 +146,7 @@ def show(imgs):
 print(img.size())
 
 tranforms = weights.transforms()
-img, _ = tranforms(img)
+img = tranforms(img)
 target = {}
 target["boxes"] = boxes
 target["labels"] = labels = torch.ones((masks.size(0),), dtype=torch.int64)
diff --git a/gallery/plot_visualization_utils.py b/gallery/plot_visualization_utils.py
index 27fd97681c0..7f92d54ebdd 100644
--- a/gallery/plot_visualization_utils.py
+++ b/gallery/plot_visualization_utils.py
@@ -81,7 +81,7 @@ def show(imgs):
 weights = FasterRCNN_ResNet50_FPN_Weights.DEFAULT
 transforms = weights.transforms()
 
-batch, _ = transforms(batch_int)
+batch = transforms(batch_int)
 
 model = fasterrcnn_resnet50_fpn(weights=weights, progress=False)
 model = model.eval()
@@ -131,7 +131,7 @@ def show(imgs):
 model = fcn_resnet50(weights=weights, progress=False)
 model = model.eval()
 
-normalized_batch, _ = transforms(batch)
+normalized_batch = transforms(batch)
 output = model(normalized_batch)['out']
 print(output.shape, output.min().item(), output.max().item())
 
@@ -272,7 +272,7 @@ def show(imgs):
 weights = MaskRCNN_ResNet50_FPN_Weights.DEFAULT
 transforms = weights.transforms()
 
-batch, _ = transforms(batch_int)
+batch = transforms(batch_int)
 
 model = maskrcnn_resnet50_fpn(weights=weights, progress=False)
 model = model.eval()
@@ -397,7 +397,7 @@ def show(imgs):
 weights = KeypointRCNN_ResNet50_FPN_Weights.DEFAULT
 transforms = weights.transforms()
 
-person_float, _ = transforms(person_int)
+person_float = transforms(person_int)
 
 model = keypointrcnn_resnet50_fpn(weights=weights, progress=False)
 model = model.eval()
diff --git a/references/classification/README.md b/references/classification/README.md
index 173fb454995..c274c997791 100644
--- a/references/classification/README.md
+++ b/references/classification/README.md
@@ -43,7 +43,7 @@ Since it expects tensors with a size of N x 3 x 299 x 299, to validate the model
 
 ```
 torchrun --nproc_per_node=8 train.py --model inception_v3\
-      --val-resize-size 342 --val-crop-size 299 --train-crop-size 299 --test-only --pretrained
+      --test-only --weights Inception_V3_Weights.IMAGENET1K_V1
 ```
 
 ### ResNet
@@ -96,22 +96,14 @@ The weights of the B5-B7 variants are ported from Luke Melas' [EfficientNet-PyTo
 
 All models were trained using Bicubic interpolation and each have custom crop and resize sizes. To validate the models use the following commands:
 ```
-torchrun --nproc_per_node=8 train.py --model efficientnet_b0 --interpolation bicubic\
-     --val-resize-size 256 --val-crop-size 224 --train-crop-size 224 --test-only --pretrained
-torchrun --nproc_per_node=8 train.py --model efficientnet_b1 --interpolation bicubic\
-      --val-resize-size 256 --val-crop-size 240 --train-crop-size 240 --test-only --pretrained
-torchrun --nproc_per_node=8 train.py --model efficientnet_b2 --interpolation bicubic\
-      --val-resize-size 288 --val-crop-size 288 --train-crop-size 288 --test-only --pretrained
-torchrun --nproc_per_node=8 train.py --model efficientnet_b3 --interpolation bicubic\
-      --val-resize-size 320 --val-crop-size 300 --train-crop-size 300 --test-only --pretrained
-torchrun --nproc_per_node=8 train.py --model efficientnet_b4 --interpolation bicubic\
-      --val-resize-size 384 --val-crop-size 380 --train-crop-size 380 --test-only --pretrained
-torchrun --nproc_per_node=8 train.py --model efficientnet_b5 --interpolation bicubic\
-      --val-resize-size 456 --val-crop-size 456 --train-crop-size 456 --test-only --pretrained
-torchrun --nproc_per_node=8 train.py --model efficientnet_b6 --interpolation bicubic\
-      --val-resize-size 528 --val-crop-size 528 --train-crop-size 528 --test-only --pretrained
-torchrun --nproc_per_node=8 train.py --model efficientnet_b7 --interpolation bicubic\
-      --val-resize-size 600 --val-crop-size 600 --train-crop-size 600 --test-only --pretrained
+torchrun --nproc_per_node=8 train.py --model efficientnet_b0 --test-only --weights EfficientNet_B0_Weights.IMAGENET1K_V1
+torchrun --nproc_per_node=8 train.py --model efficientnet_b1 --test-only --weights EfficientNet_B1_Weights.IMAGENET1K_V1
+torchrun --nproc_per_node=8 train.py --model efficientnet_b2 --test-only --weights EfficientNet_B2_Weights.IMAGENET1K_V1
+torchrun --nproc_per_node=8 train.py --model efficientnet_b3 --test-only --weights EfficientNet_B3_Weights.IMAGENET1K_V1
+torchrun --nproc_per_node=8 train.py --model efficientnet_b4 --test-only --weights EfficientNet_B4_Weights.IMAGENET1K_V1
+torchrun --nproc_per_node=8 train.py --model efficientnet_b5 --test-only --weights EfficientNet_B5_Weights.IMAGENET1K_V1
+torchrun --nproc_per_node=8 train.py --model efficientnet_b6 --test-only --weights EfficientNet_B6_Weights.IMAGENET1K_V1
+torchrun --nproc_per_node=8 train.py --model efficientnet_b7 --test-only --weights EfficientNet_B7_Weights.IMAGENET1K_V1
 ```
 
 
diff --git a/references/classification/train.py b/references/classification/train.py
index 569cf3009e7..eb8b56c1ad0 100644
--- a/references/classification/train.py
+++ b/references/classification/train.py
@@ -15,12 +15,6 @@
 from torchvision.transforms.functional import InterpolationMode
 
 
-try:
-    from torchvision import prototype
-except ImportError:
-    prototype = None
-
-
 def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, args, model_ema=None, scaler=None):
     model.train()
     metric_logger = utils.MetricLogger(delimiter="  ")
@@ -154,18 +148,13 @@ def load_data(traindir, valdir, args):
         print(f"Loading dataset_test from {cache_path}")
         dataset_test, _ = torch.load(cache_path)
     else:
-        if not args.prototype:
+        if args.weights and args.test_only:
+            weights = torchvision.models.get_weight(args.weights)
+            preprocessing = weights.transforms()
+        else:
             preprocessing = presets.ClassificationPresetEval(
                 crop_size=val_crop_size, resize_size=val_resize_size, interpolation=interpolation
             )
-        else:
-            if args.weights:
-                weights = prototype.models.get_weight(args.weights)
-                preprocessing = weights.transforms()
-            else:
-                preprocessing = prototype.transforms.ImageClassificationEval(
-                    crop_size=val_crop_size, resize_size=val_resize_size, interpolation=interpolation
-                )
 
         dataset_test = torchvision.datasets.ImageFolder(
             valdir,
@@ -191,10 +180,6 @@ def load_data(traindir, valdir, args):
 
 
 def main(args):
-    if args.prototype and prototype is None:
-        raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
-    if not args.prototype and args.weights:
-        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -236,10 +221,7 @@ def main(args):
     )
 
     print("Creating model")
-    if not args.prototype:
-        model = torchvision.models.__dict__[args.model](pretrained=args.pretrained, num_classes=num_classes)
-    else:
-        model = prototype.models.__dict__[args.model](weights=args.weights, num_classes=num_classes)
+    model = torchvision.models.__dict__[args.model](weights=args.weights, num_classes=num_classes)
     model.to(device)
 
     if args.distributed and args.sync_bn:
@@ -446,12 +428,6 @@ def get_args_parser(add_help=True):
         help="Only test the model",
         action="store_true",
     )
-    parser.add_argument(
-        "--pretrained",
-        dest="pretrained",
-        help="Use pre-trained models from the modelzoo",
-        action="store_true",
-    )
     parser.add_argument("--auto-augment", default=None, type=str, help="auto augment policy (default: None)")
     parser.add_argument("--random-erase", default=0.0, type=float, help="random erasing probability (default: 0.0)")
 
@@ -496,14 +472,6 @@ def get_args_parser(add_help=True):
     parser.add_argument(
         "--ra-reps", default=3, type=int, help="number of repetitions for Repeated Augmentation (default: 3)"
     )
-
-    # Prototype models only
-    parser.add_argument(
-        "--prototype",
-        dest="prototype",
-        help="Use prototype model builders instead those from main area",
-        action="store_true",
-    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
 
     return parser
diff --git a/references/classification/train_quantization.py b/references/classification/train_quantization.py
index 111777a860b..c0e5af1dcfc 100644
--- a/references/classification/train_quantization.py
+++ b/references/classification/train_quantization.py
@@ -12,17 +12,7 @@
 from train import train_one_epoch, evaluate, load_data
 
 
-try:
-    from torchvision import prototype
-except ImportError:
-    prototype = None
-
-
 def main(args):
-    if args.prototype and prototype is None:
-        raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
-    if not args.prototype and args.weights:
-        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -56,10 +46,7 @@ def main(args):
 
     print("Creating model", args.model)
     # when training quantized models, we always start from a pre-trained fp32 reference model
-    if not args.prototype:
-        model = torchvision.models.quantization.__dict__[args.model](pretrained=True, quantize=args.test_only)
-    else:
-        model = prototype.models.quantization.__dict__[args.model](weights=args.weights, quantize=args.test_only)
+    model = torchvision.models.quantization.__dict__[args.model](weights=args.weights, quantize=args.test_only)
     model.to(device)
 
     if not (args.test_only or args.post_training_quantize):
@@ -264,14 +251,6 @@ def get_args_parser(add_help=True):
         "--train-crop-size", default=224, type=int, help="the random crop size used for training (default: 224)"
     )
     parser.add_argument("--clip-grad-norm", default=None, type=float, help="the maximum gradient norm (default None)")
-
-    # Prototype models only
-    parser.add_argument(
-        "--prototype",
-        dest="prototype",
-        help="Use prototype model builders instead those from main area",
-        action="store_true",
-    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
 
     return parser
diff --git a/references/classification/utils.py b/references/classification/utils.py
index 7f573415c4c..27398d97234 100644
--- a/references/classification/utils.py
+++ b/references/classification/utils.py
@@ -330,22 +330,22 @@ def store_model_weights(model, checkpoint_path, checkpoint_key="model", strict=T
         from torchvision import models as M
 
         # Classification
-        model = M.mobilenet_v3_large(pretrained=False)
+        model = M.mobilenet_v3_large()
         print(store_model_weights(model, './class.pth'))
 
         # Quantized Classification
-        model = M.quantization.mobilenet_v3_large(pretrained=False, quantize=False)
+        model = M.quantization.mobilenet_v3_large(quantize=False)
         model.fuse_model(is_qat=True)
         model.qconfig = torch.ao.quantization.get_default_qat_qconfig('qnnpack')
         _ = torch.ao.quantization.prepare_qat(model, inplace=True)
         print(store_model_weights(model, './qat.pth'))
 
         # Object Detection
-        model = M.detection.fasterrcnn_mobilenet_v3_large_fpn(pretrained=False, pretrained_backbone=False)
+        model = M.detection.fasterrcnn_mobilenet_v3_large_fpn()
         print(store_model_weights(model, './obj.pth'))
 
         # Segmentation
-        model = M.segmentation.deeplabv3_mobilenet_v3_large(pretrained=False, pretrained_backbone=False, aux_loss=True)
+        model = M.segmentation.deeplabv3_mobilenet_v3_large(aux_loss=True)
         print(store_model_weights(model, './segm.pth', strict=False))
 
     Args:
diff --git a/references/detection/README.md b/references/detection/README.md
index 3695644138b..aec7c10e1b5 100644
--- a/references/detection/README.md
+++ b/references/detection/README.md
@@ -24,35 +24,35 @@ Except otherwise noted, all models have been trained on 8x V100 GPUs.
 ```
 torchrun --nproc_per_node=8 train.py\
     --dataset coco --model fasterrcnn_resnet50_fpn --epochs 26\
-    --lr-steps 16 22 --aspect-ratio-group-factor 3
+    --lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
 ```
 
 ### Faster R-CNN MobileNetV3-Large FPN
 ```
 torchrun --nproc_per_node=8 train.py\
     --dataset coco --model fasterrcnn_mobilenet_v3_large_fpn --epochs 26\
-    --lr-steps 16 22 --aspect-ratio-group-factor 3
+    --lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
 ```
 
 ### Faster R-CNN MobileNetV3-Large 320 FPN
 ```
 torchrun --nproc_per_node=8 train.py\
     --dataset coco --model fasterrcnn_mobilenet_v3_large_320_fpn --epochs 26\
-    --lr-steps 16 22 --aspect-ratio-group-factor 3
+    --lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
 ```
 
 ### FCOS ResNet-50 FPN
 ```
 torchrun --nproc_per_node=8 train.py\
     --dataset coco --model fcos_resnet50_fpn --epochs 26\
-    --lr-steps 16 22 --aspect-ratio-group-factor 3  --lr 0.01 --amp
+    --lr-steps 16 22 --aspect-ratio-group-factor 3  --lr 0.01 --amp --weights-backbone ResNet50_Weights.IMAGENET1K_V1
 ```
 
 ### RetinaNet
 ```
 torchrun --nproc_per_node=8 train.py\
     --dataset coco --model retinanet_resnet50_fpn --epochs 26\
-    --lr-steps 16 22 --aspect-ratio-group-factor 3 --lr 0.01
+    --lr-steps 16 22 --aspect-ratio-group-factor 3 --lr 0.01 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
 ```
 
 ### SSD300 VGG16
@@ -60,7 +60,7 @@ torchrun --nproc_per_node=8 train.py\
 torchrun --nproc_per_node=8 train.py\
     --dataset coco --model ssd300_vgg16 --epochs 120\
     --lr-steps 80 110 --aspect-ratio-group-factor 3 --lr 0.002 --batch-size 4\
-    --weight-decay 0.0005 --data-augmentation ssd
+    --weight-decay 0.0005 --data-augmentation ssd --weights-backbone VGG16_Weights.IMAGENET1K_FEATURES
 ```
 
 ### SSDlite320 MobileNetV3-Large
@@ -68,7 +68,7 @@ torchrun --nproc_per_node=8 train.py\
 torchrun --nproc_per_node=8 train.py\
     --dataset coco --model ssdlite320_mobilenet_v3_large --epochs 660\
     --aspect-ratio-group-factor 3 --lr-scheduler cosineannealinglr --lr 0.15 --batch-size 24\
-    --weight-decay 0.00004 --data-augmentation ssdlite
+    --weight-decay 0.00004 --data-augmentation ssdlite --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
 ```
 
 
@@ -76,7 +76,7 @@ torchrun --nproc_per_node=8 train.py\
 ```
 torchrun --nproc_per_node=8 train.py\
     --dataset coco --model maskrcnn_resnet50_fpn --epochs 26\
-    --lr-steps 16 22 --aspect-ratio-group-factor 3
+    --lr-steps 16 22 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
 ```
 
 
@@ -84,5 +84,5 @@ torchrun --nproc_per_node=8 train.py\
 ```
 torchrun --nproc_per_node=8 train.py\
     --dataset coco_kp --model keypointrcnn_resnet50_fpn --epochs 46\
-    --lr-steps 36 43 --aspect-ratio-group-factor 3
+    --lr-steps 36 43 --aspect-ratio-group-factor 3 --weights-backbone ResNet50_Weights.IMAGENET1K_V1
 ```
diff --git a/references/detection/train.py b/references/detection/train.py
index 3909e6413d0..0e0a0d70fad 100644
--- a/references/detection/train.py
+++ b/references/detection/train.py
@@ -33,12 +33,6 @@
 from group_by_aspect_ratio import GroupedBatchSampler, create_aspect_ratio_groups
 
 
-try:
-    from torchvision import prototype
-except ImportError:
-    prototype = None
-
-
 def get_dataset(name, image_set, transform, data_path):
     paths = {"coco": (data_path, get_coco, 91), "coco_kp": (data_path, get_coco_kp, 2)}
     p, ds_fn, num_classes = paths[name]
@@ -50,14 +44,12 @@ def get_dataset(name, image_set, transform, data_path):
 def get_transform(train, args):
     if train:
         return presets.DetectionPresetTrain(args.data_augmentation)
-    elif not args.prototype:
-        return presets.DetectionPresetEval()
+    elif args.weights and args.test_only:
+        weights = torchvision.models.get_weight(args.weights)
+        trans = weights.transforms()
+        return lambda img, target=None: (trans(img), target)
     else:
-        if args.weights:
-            weights = prototype.models.get_weight(args.weights)
-            return weights.transforms()
-        else:
-            return prototype.transforms.ObjectDetectionEval()
+        return presets.DetectionPresetEval()
 
 
 def get_args_parser(add_help=True):
@@ -132,25 +124,12 @@ def get_args_parser(add_help=True):
         help="Only test the model",
         action="store_true",
     )
-    parser.add_argument(
-        "--pretrained",
-        dest="pretrained",
-        help="Use pre-trained models from the modelzoo",
-        action="store_true",
-    )
 
     # distributed training parameters
     parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
     parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
-
-    # Prototype models only
-    parser.add_argument(
-        "--prototype",
-        dest="prototype",
-        help="Use prototype model builders instead those from main area",
-        action="store_true",
-    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
+    parser.add_argument("--weights-backbone", default=None, type=str, help="the backbone weights enum name to load")
 
     # Mixed precision training parameters
     parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
@@ -159,10 +138,6 @@ def get_args_parser(add_help=True):
 
 
 def main(args):
-    if args.prototype and prototype is None:
-        raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
-    if not args.prototype and args.weights:
-        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -204,12 +179,9 @@ def main(args):
     if "rcnn" in args.model:
         if args.rpn_score_thresh is not None:
             kwargs["rpn_score_thresh"] = args.rpn_score_thresh
-    if not args.prototype:
-        model = torchvision.models.detection.__dict__[args.model](
-            pretrained=args.pretrained, num_classes=num_classes, **kwargs
-        )
-    else:
-        model = prototype.models.detection.__dict__[args.model](weights=args.weights, num_classes=num_classes, **kwargs)
+    model = torchvision.models.detection.__dict__[args.model](
+        weights=args.weights, weights_backbone=args.weights_backbone, num_classes=num_classes, **kwargs
+    )
     model.to(device)
     if args.distributed and args.sync_bn:
         model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
diff --git a/references/optical_flow/README.md b/references/optical_flow/README.md
index a7620ce4be6..a7ac0223739 100644
--- a/references/optical_flow/README.md
+++ b/references/optical_flow/README.md
@@ -51,7 +51,7 @@ torchrun --nproc_per_node 8 --nnodes 1 train.py \
 ### Evaluation
 
 ```
-torchrun --nproc_per_node 1 --nnodes 1 train.py --val-dataset sintel --batch-size 1 --dataset-root $dataset_root --model raft_large --pretrained
+torchrun --nproc_per_node 1 --nnodes 1 train.py --val-dataset sintel --batch-size 1 --dataset-root $dataset_root --model raft_large --weights Raft_Large_Weights.C_T_SKHT_V2
 ```
 
 This should give an epe of about 1.3822 on the clean pass and 2.7161 on the
@@ -67,6 +67,6 @@ Sintel val final epe: 2.7161	1px: 0.8528	3px: 0.9204	5px: 0.9392	per_image_epe:
 You can also evaluate on Kitti train:
 
 ```
-torchrun --nproc_per_node 1 --nnodes 1 train.py --val-dataset kitti --batch-size 1 --dataset-root $dataset_root --model raft_large --pretrained
+torchrun --nproc_per_node 1 --nnodes 1 train.py --val-dataset kitti --batch-size 1 --dataset-root $dataset_root --model raft_large --weights Raft_Large_Weights.C_T_SKHT_V2
 Kitti val epe: 4.7968	1px: 0.6388	3px: 0.8197	5px: 0.8661	per_image_epe: 4.5118	f1: 16.0679
 ```
diff --git a/references/optical_flow/train.py b/references/optical_flow/train.py
index 83952242eb9..1a50d1c617d 100644
--- a/references/optical_flow/train.py
+++ b/references/optical_flow/train.py
@@ -9,11 +9,6 @@
 from presets import OpticalFlowPresetTrain, OpticalFlowPresetEval
 from torchvision.datasets import KittiFlow, FlyingChairs, FlyingThings3D, Sintel, HD1K
 
-try:
-    from torchvision import prototype
-except ImportError:
-    prototype = None
-
 
 def get_train_dataset(stage, dataset_root):
     if stage == "chairs":
@@ -138,12 +133,10 @@ def inner_loop(blob):
 def evaluate(model, args):
     val_datasets = args.val_dataset or []
 
-    if args.prototype:
-        if args.weights:
-            weights = prototype.models.get_weight(args.weights)
-            preprocessing = weights.transforms()
-        else:
-            preprocessing = prototype.transforms.OpticalFlowEval()
+    if args.weights and args.test_only:
+        weights = torchvision.models.get_weight(args.weights)
+        trans = weights.transforms()
+        preprocessing = lambda img1, img2, flow=None, valid=None: trans(img1, img2) + (flow, valid)  # noqa: E731
     else:
         preprocessing = OpticalFlowPresetEval()
 
@@ -201,20 +194,14 @@ def train_one_epoch(model, optimizer, scheduler, train_loader, logger, args):
 
 
 def main(args):
-    if args.prototype and prototype is None:
-        raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
-    if not args.prototype and args.weights:
-        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     utils.setup_ddp(args)
+    args.test_only = args.train_dataset is None
 
     if args.distributed and args.device == "cpu":
         raise ValueError("The device must be cuda if we want to run in distributed mode using torchrun")
     device = torch.device(args.device)
 
-    if args.prototype:
-        model = prototype.models.optical_flow.__dict__[args.model](weights=args.weights)
-    else:
-        model = torchvision.models.optical_flow.__dict__[args.model](pretrained=args.pretrained)
+    model = torchvision.models.optical_flow.__dict__[args.model](weights=args.weights)
 
     if args.distributed:
         model = model.to(args.local_rank)
@@ -228,7 +215,7 @@ def main(args):
         checkpoint = torch.load(args.resume, map_location="cpu")
         model_without_ddp.load_state_dict(checkpoint["model"])
 
-    if args.train_dataset is None:
+    if args.test_only:
         # Set deterministic CUDNN algorithms, since they can affect epe a fair bit.
         torch.backends.cudnn.benchmark = False
         torch.backends.cudnn.deterministic = True
@@ -356,8 +343,7 @@ def get_args_parser(add_help=True):
     parser.add_argument(
         "--model", type=str, default="raft_large", help="The name of the model to use - either raft_large or raft_small"
     )
-    # TODO: resume, pretrained, and weights should be in an exclusive arg group
-    parser.add_argument("--pretrained", action="store_true", help="Whether to use pretrained weights")
+    # TODO: resume and weights should be in an exclusive arg group
 
     parser.add_argument(
         "--num_flow_updates",
@@ -376,13 +362,6 @@ def get_args_parser(add_help=True):
         required=True,
     )
 
-    # Prototype models only
-    parser.add_argument(
-        "--prototype",
-        dest="prototype",
-        help="Use prototype model builders instead those from main area",
-        action="store_true",
-    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load.")
     parser.add_argument("--device", default="cuda", type=str, help="device (Use cuda or cpu, Default: cuda)")
 
diff --git a/references/segmentation/README.md b/references/segmentation/README.md
index e9b5391215a..2c7391c8380 100644
--- a/references/segmentation/README.md
+++ b/references/segmentation/README.md
@@ -14,30 +14,30 @@ You must modify the following flags:
 
 ## fcn_resnet50
 ```
-torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet50 --aux-loss
+torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet50 --aux-loss --weights-backbone ResNet50_Weights.IMAGENET1K_V1
 ```
 
 ## fcn_resnet101
 ```
-torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet101 --aux-loss
+torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model fcn_resnet101 --aux-loss --weights-backbone ResNet101_Weights.IMAGENET1K_V1
 ```
 
 ## deeplabv3_resnet50
 ```
-torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet50 --aux-loss
+torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet50 --aux-loss --weights-backbone ResNet50_Weights.IMAGENET1K_V1
 ```
 
 ## deeplabv3_resnet101
 ```
-torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet101 --aux-loss
+torchrun --nproc_per_node=8 train.py --lr 0.02 --dataset coco -b 4 --model deeplabv3_resnet101 --aux-loss --weights-backbone ResNet101_Weights.IMAGENET1K_V1
 ```
 
 ## deeplabv3_mobilenet_v3_large
 ```
-torchrun --nproc_per_node=8 train.py --dataset coco -b 4 --model deeplabv3_mobilenet_v3_large --aux-loss --wd 0.000001
+torchrun --nproc_per_node=8 train.py --dataset coco -b 4 --model deeplabv3_mobilenet_v3_large --aux-loss --wd 0.000001 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
 ```
 
 ## lraspp_mobilenet_v3_large
 ```
-torchrun --nproc_per_node=8 train.py --dataset coco -b 4 --model lraspp_mobilenet_v3_large --wd 0.000001
+torchrun --nproc_per_node=8 train.py --dataset coco -b 4 --model lraspp_mobilenet_v3_large --wd 0.000001 --weights-backbone MobileNet_V3_Large_Weights.IMAGENET1K_V1
 ```
diff --git a/references/segmentation/train.py b/references/segmentation/train.py
index 5dc03945bd7..b4e55acd407 100644
--- a/references/segmentation/train.py
+++ b/references/segmentation/train.py
@@ -11,12 +11,6 @@
 from torch import nn
 
 
-try:
-    from torchvision import prototype
-except ImportError:
-    prototype = None
-
-
 def get_dataset(dir_path, name, image_set, transform):
     def sbd(*args, **kwargs):
         return torchvision.datasets.SBDataset(*args, mode="segmentation", **kwargs)
@@ -35,14 +29,12 @@ def sbd(*args, **kwargs):
 def get_transform(train, args):
     if train:
         return presets.SegmentationPresetTrain(base_size=520, crop_size=480)
-    elif not args.prototype:
-        return presets.SegmentationPresetEval(base_size=520)
+    elif args.weights and args.test_only:
+        weights = torchvision.models.get_weight(args.weights)
+        trans = weights.transforms()
+        return lambda img, target=None: (trans(img), target)
     else:
-        if args.weights:
-            weights = prototype.models.get_weight(args.weights)
-            return weights.transforms()
-        else:
-            return prototype.transforms.SemanticSegmentationEval(resize_size=520)
+        return presets.SegmentationPresetEval(base_size=520)
 
 
 def criterion(inputs, target):
@@ -100,10 +92,6 @@ def train_one_epoch(model, criterion, optimizer, data_loader, lr_scheduler, devi
 
 
 def main(args):
-    if args.prototype and prototype is None:
-        raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
-    if not args.prototype and args.weights:
-        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -135,16 +123,9 @@ def main(args):
         dataset_test, batch_size=1, sampler=test_sampler, num_workers=args.workers, collate_fn=utils.collate_fn
     )
 
-    if not args.prototype:
-        model = torchvision.models.segmentation.__dict__[args.model](
-            pretrained=args.pretrained,
-            num_classes=num_classes,
-            aux_loss=args.aux_loss,
-        )
-    else:
-        model = prototype.models.segmentation.__dict__[args.model](
-            weights=args.weights, num_classes=num_classes, aux_loss=args.aux_loss
-        )
+    model = torchvision.models.segmentation.__dict__[args.model](
+        weights=args.weights, weights_backbone=args.weights_backbone, num_classes=num_classes, aux_loss=args.aux_loss
+    )
     model.to(device)
     if args.distributed:
         model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
@@ -272,24 +253,12 @@ def get_args_parser(add_help=True):
         help="Only test the model",
         action="store_true",
     )
-    parser.add_argument(
-        "--pretrained",
-        dest="pretrained",
-        help="Use pre-trained models from the modelzoo",
-        action="store_true",
-    )
     # distributed training parameters
     parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
     parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
 
-    # Prototype models only
-    parser.add_argument(
-        "--prototype",
-        dest="prototype",
-        help="Use prototype model builders instead those from main area",
-        action="store_true",
-    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
+    parser.add_argument("--weights-backbone", default=None, type=str, help="the backbone weights enum name to load")
 
     # Mixed precision training parameters
     parser.add_argument("--amp", action="store_true", help="Use torch.cuda.amp for mixed precision training")
diff --git a/references/video_classification/presets.py b/references/video_classification/presets.py
index 04039c9a4f1..d24169e42dd 100644
--- a/references/video_classification/presets.py
+++ b/references/video_classification/presets.py
@@ -6,8 +6,8 @@
 class VideoClassificationPresetTrain:
     def __init__(
         self,
-        resize_size,
         crop_size,
+        resize_size,
         mean=(0.43216, 0.394666, 0.37645),
         std=(0.22803, 0.22145, 0.216989),
         hflip_prob=0.5,
@@ -27,7 +27,7 @@ def __call__(self, x):
 
 
 class VideoClassificationPresetEval:
-    def __init__(self, resize_size, crop_size, mean=(0.43216, 0.394666, 0.37645), std=(0.22803, 0.22145, 0.216989)):
+    def __init__(self, crop_size, resize_size, mean=(0.43216, 0.394666, 0.37645), std=(0.22803, 0.22145, 0.216989)):
         self.transforms = transforms.Compose(
             [
                 ConvertBHWCtoBCHW(),
diff --git a/references/video_classification/train.py b/references/video_classification/train.py
index d36785ddf96..da7ef9fc607 100644
--- a/references/video_classification/train.py
+++ b/references/video_classification/train.py
@@ -12,11 +12,6 @@
 from torch.utils.data.dataloader import default_collate
 from torchvision.datasets.samplers import DistributedSampler, UniformClipSampler, RandomClipSampler
 
-try:
-    from torchvision import prototype
-except ImportError:
-    prototype = None
-
 
 def train_one_epoch(model, criterion, optimizer, lr_scheduler, data_loader, device, epoch, print_freq, scaler=None):
     model.train()
@@ -96,10 +91,6 @@ def collate_fn(batch):
 
 
 def main(args):
-    if args.prototype and prototype is None:
-        raise ImportError("The prototype module couldn't be found. Please install the latest torchvision nightly.")
-    if not args.prototype and args.weights:
-        raise ValueError("The weights parameter works only in prototype mode. Please pass the --prototype argument.")
     if args.output_dir:
         utils.mkdir(args.output_dir)
 
@@ -120,7 +111,7 @@ def main(args):
     print("Loading training data")
     st = time.time()
     cache_path = _get_cache_path(traindir)
-    transform_train = presets.VideoClassificationPresetTrain((128, 171), (112, 112))
+    transform_train = presets.VideoClassificationPresetTrain(crop_size=(112, 112), resize_size=(128, 171))
 
     if args.cache_dataset and os.path.exists(cache_path):
         print(f"Loading dataset_train from {cache_path}")
@@ -150,14 +141,11 @@ def main(args):
     print("Loading validation data")
     cache_path = _get_cache_path(valdir)
 
-    if not args.prototype:
-        transform_test = presets.VideoClassificationPresetEval(resize_size=(128, 171), crop_size=(112, 112))
+    if args.weights and args.test_only:
+        weights = torchvision.models.get_weight(args.weights)
+        transform_test = weights.transforms()
     else:
-        if args.weights:
-            weights = prototype.models.get_weight(args.weights)
-            transform_test = weights.transforms()
-        else:
-            transform_test = prototype.transforms.VideoClassificationEval(crop_size=(112, 112), resize_size=(128, 171))
+        transform_test = presets.VideoClassificationPresetEval(crop_size=(112, 112), resize_size=(128, 171))
 
     if args.cache_dataset and os.path.exists(cache_path):
         print(f"Loading dataset_test from {cache_path}")
@@ -208,10 +196,7 @@ def main(args):
     )
 
     print("Creating model")
-    if not args.prototype:
-        model = torchvision.models.video.__dict__[args.model](pretrained=args.pretrained)
-    else:
-        model = prototype.models.video.__dict__[args.model](weights=args.weights)
+    model = torchvision.models.video.__dict__[args.model](weights=args.weights)
     model.to(device)
     if args.distributed and args.sync_bn:
         model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
@@ -352,24 +337,11 @@ def parse_args():
         help="Only test the model",
         action="store_true",
     )
-    parser.add_argument(
-        "--pretrained",
-        dest="pretrained",
-        help="Use pre-trained models from the modelzoo",
-        action="store_true",
-    )
 
     # distributed training parameters
     parser.add_argument("--world-size", default=1, type=int, help="number of distributed processes")
     parser.add_argument("--dist-url", default="env://", type=str, help="url used to set up distributed training")
 
-    # Prototype models only
-    parser.add_argument(
-        "--prototype",
-        dest="prototype",
-        help="Use prototype model builders instead those from main area",
-        action="store_true",
-    )
     parser.add_argument("--weights", default=None, type=str, help="the weights enum name to load")
 
     # Mixed precision training parameters
diff --git a/test/test_extended_models.py b/test/test_extended_models.py
index 4bfe03d1ea0..a07b501e15b 100644
--- a/test/test_extended_models.py
+++ b/test/test_extended_models.py
@@ -3,12 +3,14 @@
 
 import pytest
 import test_models as TM
+import torch
 from torchvision import models
 from torchvision.models._api import WeightsEnum, Weights
 from torchvision.models._utils import handle_legacy_interface
 
-run_if_test_with_prototype = pytest.mark.skipif(
-    os.getenv("PYTORCH_TEST_WITH_EXTENDED") != "1",
+
+run_if_test_with_extended = pytest.mark.skipif(
+    os.getenv("PYTORCH_TEST_WITH_EXTENDED", "0") != "1",
     reason="Extended tests are disabled by default. Set PYTORCH_TEST_WITH_EXTENDED=1 to run them.",
 )
 
@@ -76,7 +78,7 @@ def test_naming_conventions(model_fn):
     + TM.get_models_from_module(models.video)
     + TM.get_models_from_module(models.optical_flow),
 )
-@run_if_test_with_prototype
+@run_if_test_with_extended
 def test_schema_meta_validation(model_fn):
     classification_fields = ["size", "categories", "acc@1", "acc@5", "min_size"]
     defaults = {
@@ -123,6 +125,63 @@ def test_schema_meta_validation(model_fn):
     assert not bad_names
 
 
+@pytest.mark.parametrize(
+    "model_fn",
+    TM.get_models_from_module(models)
+    + TM.get_models_from_module(models.detection)
+    + TM.get_models_from_module(models.quantization)
+    + TM.get_models_from_module(models.segmentation)
+    + TM.get_models_from_module(models.video)
+    + TM.get_models_from_module(models.optical_flow),
+)
+@run_if_test_with_extended
+def test_transforms_jit(model_fn):
+    model_name = model_fn.__name__
+    weights_enum = _get_model_weights(model_fn)
+    if len(weights_enum) == 0:
+        pytest.skip(f"Model '{model_name}' doesn't have any pre-trained weights.")
+
+    defaults = {
+        "models": {
+            "input_shape": (1, 3, 224, 224),
+        },
+        "detection": {
+            "input_shape": (3, 300, 300),
+        },
+        "quantization": {
+            "input_shape": (1, 3, 224, 224),
+        },
+        "segmentation": {
+            "input_shape": (1, 3, 520, 520),
+        },
+        "video": {
+            "input_shape": (1, 4, 112, 112, 3),
+        },
+        "optical_flow": {
+            "input_shape": (1, 3, 128, 128),
+        },
+    }
+    module_name = model_fn.__module__.split(".")[-2]
+
+    kwargs = {**defaults[module_name], **TM._model_params.get(model_name, {})}
+    input_shape = kwargs.pop("input_shape")
+    x = torch.rand(input_shape)
+    if module_name == "optical_flow":
+        args = (x, x)
+    else:
+        args = (x,)
+
+    problematic_weights = []
+    for w in weights_enum:
+        transforms = w.transforms()
+        try:
+            TM._check_jit_scriptable(transforms, args)
+        except Exception:
+            problematic_weights.append(w)
+
+    assert not problematic_weights
+
+
 # With this filter, every unexpected warning will be turned into an error
 @pytest.mark.filterwarnings("error")
 class TestHandleLegacyInterface:
diff --git a/test/test_models.py b/test/test_models.py
index 5bef9e24d9f..0d45d61df13 100644
--- a/test/test_models.py
+++ b/test/test_models.py
@@ -133,8 +133,7 @@ def get_export_import_copy(m):
 
     if eager_out is None:
         with torch.no_grad(), freeze_rng_state():
-            if unwrapper:
-                eager_out = nn_module(*args)
+            eager_out = nn_module(*args)
 
     with torch.no_grad(), freeze_rng_state():
         script_out = sm(*args)
diff --git a/torchvision/models/alexnet.py b/torchvision/models/alexnet.py
index 4df533000f9..6ee5b98c673 100644
--- a/torchvision/models/alexnet.py
+++ b/torchvision/models/alexnet.py
@@ -4,7 +4,7 @@
 import torch
 import torch.nn as nn
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -55,7 +55,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
 class AlexNet_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/alexnet-owt-7be5be79.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             "task": "image_classification",
             "architecture": "AlexNet",
diff --git a/torchvision/models/convnext.py b/torchvision/models/convnext.py
index 8d25e77eaa1..8774b9a1bc2 100644
--- a/torchvision/models/convnext.py
+++ b/torchvision/models/convnext.py
@@ -7,7 +7,7 @@
 
 from ..ops.misc import Conv2dNormActivation
 from ..ops.stochastic_depth import StochasticDepth
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -218,7 +218,7 @@ def _convnext(
 class ConvNeXt_Tiny_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_tiny-983f1562.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=236),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=236),
         meta={
             **_COMMON_META,
             "num_params": 28589128,
@@ -232,7 +232,7 @@ class ConvNeXt_Tiny_Weights(WeightsEnum):
 class ConvNeXt_Small_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_small-0c510722.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=230),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=230),
         meta={
             **_COMMON_META,
             "num_params": 50223688,
@@ -246,7 +246,7 @@ class ConvNeXt_Small_Weights(WeightsEnum):
 class ConvNeXt_Base_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_base-6075fbad.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 88591464,
@@ -260,7 +260,7 @@ class ConvNeXt_Base_Weights(WeightsEnum):
 class ConvNeXt_Large_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/convnext_large-ea097f82.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 197767336,
diff --git a/torchvision/models/densenet.py b/torchvision/models/densenet.py
index b0de4529902..2ffb29c54cb 100644
--- a/torchvision/models/densenet.py
+++ b/torchvision/models/densenet.py
@@ -9,7 +9,7 @@
 import torch.utils.checkpoint as cp
 from torch import Tensor
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -280,7 +280,7 @@ def _densenet(
 class DenseNet121_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet121-a639ec97.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 7978856,
@@ -294,7 +294,7 @@ class DenseNet121_Weights(WeightsEnum):
 class DenseNet161_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet161-8d451a50.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 28681000,
@@ -308,7 +308,7 @@ class DenseNet161_Weights(WeightsEnum):
 class DenseNet169_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet169-b2777c0a.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 14149480,
@@ -322,7 +322,7 @@ class DenseNet169_Weights(WeightsEnum):
 class DenseNet201_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/densenet201-c1103571.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 20013928,
diff --git a/torchvision/models/detection/faster_rcnn.py b/torchvision/models/detection/faster_rcnn.py
index b5a1df4502c..7d18fbe90a3 100644
--- a/torchvision/models/detection/faster_rcnn.py
+++ b/torchvision/models/detection/faster_rcnn.py
@@ -5,7 +5,7 @@
 from torchvision.ops import MultiScaleRoIAlign
 
 from ...ops import misc as misc_nn_ops
-from ...transforms import ObjectDetectionEval, InterpolationMode
+from ...transforms._presets import ObjectDetection, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_value_param
@@ -336,7 +336,7 @@ def forward(self, x):
 class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_resnet50_fpn_coco-258fb6c6.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             **_COMMON_META,
             "num_params": 41755286,
@@ -350,7 +350,7 @@ class FasterRCNN_ResNet50_FPN_Weights(WeightsEnum):
 class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_fpn-fb6a3cc7.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             **_COMMON_META,
             "num_params": 19386354,
@@ -364,7 +364,7 @@ class FasterRCNN_MobileNet_V3_Large_FPN_Weights(WeightsEnum):
 class FasterRCNN_MobileNet_V3_Large_320_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fasterrcnn_mobilenet_v3_large_320_fpn-907ea3f9.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             **_COMMON_META,
             "num_params": 19386354,
diff --git a/torchvision/models/detection/fcos.py b/torchvision/models/detection/fcos.py
index 7948cd76ab2..27e54a565f2 100644
--- a/torchvision/models/detection/fcos.py
+++ b/torchvision/models/detection/fcos.py
@@ -11,7 +11,7 @@
 from ...ops import boxes as box_ops
 from ...ops import misc as misc_nn_ops
 from ...ops.feature_pyramid_network import LastLevelP6P7
-from ...transforms import ObjectDetectionEval, InterpolationMode
+from ...transforms._presets import ObjectDetection, InterpolationMode
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
@@ -646,7 +646,7 @@ def forward(
 class FCOS_ResNet50_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/fcos_resnet50_fpn_coco-99b0c9b7.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             "task": "image_object_detection",
             "architecture": "FCOS",
diff --git a/torchvision/models/detection/keypoint_rcnn.py b/torchvision/models/detection/keypoint_rcnn.py
index 522545293a0..2a554a6f56e 100644
--- a/torchvision/models/detection/keypoint_rcnn.py
+++ b/torchvision/models/detection/keypoint_rcnn.py
@@ -5,7 +5,7 @@
 from torchvision.ops import MultiScaleRoIAlign
 
 from ...ops import misc as misc_nn_ops
-from ...transforms import ObjectDetectionEval, InterpolationMode
+from ...transforms._presets import ObjectDetection, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_PERSON_CATEGORIES, _COCO_PERSON_KEYPOINT_NAMES
 from .._utils import handle_legacy_interface, _ovewrite_value_param
@@ -318,7 +318,7 @@ def forward(self, x):
 class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
     COCO_LEGACY = Weights(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-9f466800.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             **_COMMON_META,
             "num_params": 59137258,
@@ -329,7 +329,7 @@ class KeypointRCNN_ResNet50_FPN_Weights(WeightsEnum):
     )
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/keypointrcnn_resnet50_fpn_coco-fc266e95.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             **_COMMON_META,
             "num_params": 59137258,
diff --git a/torchvision/models/detection/mask_rcnn.py b/torchvision/models/detection/mask_rcnn.py
index 47b32984991..fb60ffcbb0a 100644
--- a/torchvision/models/detection/mask_rcnn.py
+++ b/torchvision/models/detection/mask_rcnn.py
@@ -5,7 +5,7 @@
 from torchvision.ops import MultiScaleRoIAlign
 
 from ...ops import misc as misc_nn_ops
-from ...transforms import ObjectDetectionEval, InterpolationMode
+from ...transforms._presets import ObjectDetection, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_value_param
@@ -308,7 +308,7 @@ def __init__(self, in_channels, dim_reduced, num_classes):
 class MaskRCNN_ResNet50_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/maskrcnn_resnet50_fpn_coco-bf2d0c1e.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             "task": "image_object_detection",
             "architecture": "MaskRCNN",
diff --git a/torchvision/models/detection/retinanet.py b/torchvision/models/detection/retinanet.py
index 048d68b52dc..49b9acf45e4 100644
--- a/torchvision/models/detection/retinanet.py
+++ b/torchvision/models/detection/retinanet.py
@@ -10,7 +10,7 @@
 from ...ops import boxes as box_ops
 from ...ops import misc as misc_nn_ops
 from ...ops.feature_pyramid_network import LastLevelP6P7
-from ...transforms import ObjectDetectionEval, InterpolationMode
+from ...transforms._presets import ObjectDetection, InterpolationMode
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
@@ -588,7 +588,7 @@ def forward(self, images, targets=None):
 class RetinaNet_ResNet50_FPN_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/retinanet_resnet50_fpn_coco-eeacb38b.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             "task": "image_object_detection",
             "architecture": "RetinaNet",
diff --git a/torchvision/models/detection/ssd.py b/torchvision/models/detection/ssd.py
index b907b3fccf8..c30919e621c 100644
--- a/torchvision/models/detection/ssd.py
+++ b/torchvision/models/detection/ssd.py
@@ -7,7 +7,7 @@
 from torch import nn, Tensor
 
 from ...ops import boxes as box_ops
-from ...transforms import ObjectDetectionEval, InterpolationMode
+from ...transforms._presets import ObjectDetection, InterpolationMode
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _COCO_CATEGORIES
@@ -28,7 +28,7 @@
 class SSD300_VGG16_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/ssd300_vgg16_coco-b556d3b4.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             "task": "image_object_detection",
             "architecture": "SSD",
diff --git a/torchvision/models/detection/ssdlite.py b/torchvision/models/detection/ssdlite.py
index dad28cfed13..93023337d11 100644
--- a/torchvision/models/detection/ssdlite.py
+++ b/torchvision/models/detection/ssdlite.py
@@ -7,7 +7,7 @@
 from torch import nn, Tensor
 
 from ...ops.misc import Conv2dNormActivation
-from ...transforms import ObjectDetectionEval, InterpolationMode
+from ...transforms._presets import ObjectDetection, InterpolationMode
 from ...utils import _log_api_usage_once
 from .. import mobilenet
 from .._api import WeightsEnum, Weights
@@ -187,7 +187,7 @@ def _mobilenet_extractor(
 class SSDLite320_MobileNet_V3_Large_Weights(WeightsEnum):
     COCO_V1 = Weights(
         url="https://download.pytorch.org/models/ssdlite320_mobilenet_v3_large_coco-a79551df.pth",
-        transforms=ObjectDetectionEval,
+        transforms=ObjectDetection,
         meta={
             "task": "image_object_detection",
             "architecture": "SSDLite",
diff --git a/torchvision/models/efficientnet.py b/torchvision/models/efficientnet.py
index 9665c169bbf..b9d3b9b30c9 100644
--- a/torchvision/models/efficientnet.py
+++ b/torchvision/models/efficientnet.py
@@ -10,7 +10,7 @@
 from torchvision.ops import StochasticDepth
 
 from ..ops.misc import Conv2dNormActivation, SqueezeExcitation
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -458,7 +458,7 @@ class EfficientNet_B0_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b0_rwightman-3dd342df.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=224, resize_size=256, interpolation=InterpolationMode.BICUBIC
+            ImageClassification, crop_size=224, resize_size=256, interpolation=InterpolationMode.BICUBIC
         ),
         meta={
             **_COMMON_META_V1,
@@ -475,7 +475,7 @@ class EfficientNet_B1_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b1_rwightman-533bc792.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=240, resize_size=256, interpolation=InterpolationMode.BICUBIC
+            ImageClassification, crop_size=240, resize_size=256, interpolation=InterpolationMode.BICUBIC
         ),
         meta={
             **_COMMON_META_V1,
@@ -488,7 +488,7 @@ class EfficientNet_B1_Weights(WeightsEnum):
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b1-c27df63c.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=240, resize_size=255, interpolation=InterpolationMode.BILINEAR
+            ImageClassification, crop_size=240, resize_size=255, interpolation=InterpolationMode.BILINEAR
         ),
         meta={
             **_COMMON_META_V1,
@@ -507,7 +507,7 @@ class EfficientNet_B2_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b2_rwightman-bcdf34b7.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=288, resize_size=288, interpolation=InterpolationMode.BICUBIC
+            ImageClassification, crop_size=288, resize_size=288, interpolation=InterpolationMode.BICUBIC
         ),
         meta={
             **_COMMON_META_V1,
@@ -524,7 +524,7 @@ class EfficientNet_B3_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b3_rwightman-cf984f9c.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=300, resize_size=320, interpolation=InterpolationMode.BICUBIC
+            ImageClassification, crop_size=300, resize_size=320, interpolation=InterpolationMode.BICUBIC
         ),
         meta={
             **_COMMON_META_V1,
@@ -541,7 +541,7 @@ class EfficientNet_B4_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b4_rwightman-7eb33cd5.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=380, resize_size=384, interpolation=InterpolationMode.BICUBIC
+            ImageClassification, crop_size=380, resize_size=384, interpolation=InterpolationMode.BICUBIC
         ),
         meta={
             **_COMMON_META_V1,
@@ -558,7 +558,7 @@ class EfficientNet_B5_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b5_lukemelas-b6417697.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=456, resize_size=456, interpolation=InterpolationMode.BICUBIC
+            ImageClassification, crop_size=456, resize_size=456, interpolation=InterpolationMode.BICUBIC
         ),
         meta={
             **_COMMON_META_V1,
@@ -575,7 +575,7 @@ class EfficientNet_B6_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b6_lukemelas-c76e70fd.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=528, resize_size=528, interpolation=InterpolationMode.BICUBIC
+            ImageClassification, crop_size=528, resize_size=528, interpolation=InterpolationMode.BICUBIC
         ),
         meta={
             **_COMMON_META_V1,
@@ -592,7 +592,7 @@ class EfficientNet_B7_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_b7_lukemelas-dcc49843.pth",
         transforms=partial(
-            ImageClassificationEval, crop_size=600, resize_size=600, interpolation=InterpolationMode.BICUBIC
+            ImageClassification, crop_size=600, resize_size=600, interpolation=InterpolationMode.BICUBIC
         ),
         meta={
             **_COMMON_META_V1,
@@ -609,7 +609,7 @@ class EfficientNet_V2_S_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_v2_s-dd5fe13b.pth",
         transforms=partial(
-            ImageClassificationEval,
+            ImageClassification,
             crop_size=384,
             resize_size=384,
             interpolation=InterpolationMode.BILINEAR,
@@ -629,7 +629,7 @@ class EfficientNet_V2_M_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_v2_m-dc08266a.pth",
         transforms=partial(
-            ImageClassificationEval,
+            ImageClassification,
             crop_size=480,
             resize_size=480,
             interpolation=InterpolationMode.BILINEAR,
@@ -649,7 +649,7 @@ class EfficientNet_V2_L_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/efficientnet_v2_l-59c71312.pth",
         transforms=partial(
-            ImageClassificationEval,
+            ImageClassification,
             crop_size=480,
             resize_size=480,
             interpolation=InterpolationMode.BICUBIC,
diff --git a/torchvision/models/googlenet.py b/torchvision/models/googlenet.py
index e09e6788097..ced92571974 100644
--- a/torchvision/models/googlenet.py
+++ b/torchvision/models/googlenet.py
@@ -8,7 +8,7 @@
 import torch.nn.functional as F
 from torch import Tensor
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -278,7 +278,7 @@ def forward(self, x: Tensor) -> Tensor:
 class GoogLeNet_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/googlenet-1378be20.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             "task": "image_classification",
             "architecture": "GoogLeNet",
diff --git a/torchvision/models/inception.py b/torchvision/models/inception.py
index 24d084b62d2..816fab45549 100644
--- a/torchvision/models/inception.py
+++ b/torchvision/models/inception.py
@@ -7,7 +7,7 @@
 import torch.nn.functional as F
 from torch import nn, Tensor
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -410,7 +410,7 @@ def forward(self, x: Tensor) -> Tensor:
 class Inception_V3_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/inception_v3_google-0cc3c7bd.pth",
-        transforms=partial(ImageClassificationEval, crop_size=299, resize_size=342),
+        transforms=partial(ImageClassification, crop_size=299, resize_size=342),
         meta={
             "task": "image_classification",
             "architecture": "InceptionV3",
diff --git a/torchvision/models/mnasnet.py b/torchvision/models/mnasnet.py
index 287911edbec..578e77f7934 100644
--- a/torchvision/models/mnasnet.py
+++ b/torchvision/models/mnasnet.py
@@ -6,7 +6,7 @@
 import torch.nn as nn
 from torch import Tensor
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -226,7 +226,7 @@ def _load_from_state_dict(
 class MNASNet0_5_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/mnasnet0.5_top1_67.823-3ffadce67e.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 2218512,
@@ -245,7 +245,7 @@ class MNASNet0_75_Weights(WeightsEnum):
 class MNASNet1_0_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/mnasnet1.0_top1_73.512-f206786ef8.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 4383312,
diff --git a/torchvision/models/mobilenetv2.py b/torchvision/models/mobilenetv2.py
index 1e19db1a314..085049117ec 100644
--- a/torchvision/models/mobilenetv2.py
+++ b/torchvision/models/mobilenetv2.py
@@ -7,7 +7,7 @@
 from torch import nn
 
 from ..ops.misc import Conv2dNormActivation
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -209,7 +209,7 @@ def forward(self, x: Tensor) -> Tensor:
 class MobileNet_V2_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/mobilenet_v2-b0353104.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/tree/main/references/classification#mobilenetv2",
@@ -219,7 +219,7 @@ class MobileNet_V2_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/mobilenet_v2-7ebf99e0.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "recipe": "https://github.com/pytorch/vision/issues/3995#new-recipe-with-reg-tuning",
diff --git a/torchvision/models/mobilenetv3.py b/torchvision/models/mobilenetv3.py
index 3a98456416d..91e1ea91a94 100644
--- a/torchvision/models/mobilenetv3.py
+++ b/torchvision/models/mobilenetv3.py
@@ -6,7 +6,7 @@
 from torch import nn, Tensor
 
 from ..ops.misc import Conv2dNormActivation, SqueezeExcitation as SElayer
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -317,7 +317,7 @@ def _mobilenet_v3(
 class MobileNet_V3_Large_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/mobilenet_v3_large-8738ca79.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 5483032,
@@ -328,7 +328,7 @@ class MobileNet_V3_Large_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/mobilenet_v3_large-5c1a4163.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 5483032,
@@ -343,7 +343,7 @@ class MobileNet_V3_Large_Weights(WeightsEnum):
 class MobileNet_V3_Small_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/mobilenet_v3_small-047dcff4.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 2542856,
diff --git a/torchvision/models/optical_flow/raft.py b/torchvision/models/optical_flow/raft.py
index a506224d4b3..244d2b2fac1 100644
--- a/torchvision/models/optical_flow/raft.py
+++ b/torchvision/models/optical_flow/raft.py
@@ -8,7 +8,7 @@
 from torch.nn.modules.instancenorm import InstanceNorm2d
 from torchvision.ops import Conv2dNormActivation
 
-from ...transforms import OpticalFlowEval, InterpolationMode
+from ...transforms._presets import OpticalFlow, InterpolationMode
 from ...utils import _log_api_usage_once
 from .._api import Weights, WeightsEnum
 from .._utils import handle_legacy_interface
@@ -523,7 +523,7 @@ class Raft_Large_Weights(WeightsEnum):
     C_T_V1 = Weights(
         # Chairs + Things, ported from original paper repo (raft-things.pth)
         url="https://download.pytorch.org/models/raft_large_C_T_V1-22a6c225.pth",
-        transforms=OpticalFlowEval,
+        transforms=OpticalFlow,
         meta={
             **_COMMON_META,
             "num_params": 5257536,
@@ -538,7 +538,7 @@ class Raft_Large_Weights(WeightsEnum):
     C_T_V2 = Weights(
         # Chairs + Things
         url="https://download.pytorch.org/models/raft_large_C_T_V2-1bb1363a.pth",
-        transforms=OpticalFlowEval,
+        transforms=OpticalFlow,
         meta={
             **_COMMON_META,
             "num_params": 5257536,
@@ -553,7 +553,7 @@ class Raft_Large_Weights(WeightsEnum):
     C_T_SKHT_V1 = Weights(
         # Chairs + Things + Sintel fine-tuning, ported from original paper repo (raft-sintel.pth)
         url="https://download.pytorch.org/models/raft_large_C_T_SKHT_V1-0b8c9e55.pth",
-        transforms=OpticalFlowEval,
+        transforms=OpticalFlow,
         meta={
             **_COMMON_META,
             "num_params": 5257536,
@@ -568,7 +568,7 @@ class Raft_Large_Weights(WeightsEnum):
         # Chairs + Things + (Sintel + Kitti + HD1K + Things_clean)
         # Corresponds to the C+T+S+K+H on paper with fine-tuning on Sintel
         url="https://download.pytorch.org/models/raft_large_C_T_SKHT_V2-ff5fadd5.pth",
-        transforms=OpticalFlowEval,
+        transforms=OpticalFlow,
         meta={
             **_COMMON_META,
             "num_params": 5257536,
@@ -581,7 +581,7 @@ class Raft_Large_Weights(WeightsEnum):
     C_T_SKHT_K_V1 = Weights(
         # Chairs + Things + Sintel fine-tuning + Kitti fine-tuning, ported from the original repo (sintel-kitti.pth)
         url="https://download.pytorch.org/models/raft_large_C_T_SKHT_K_V1-4a6a5039.pth",
-        transforms=OpticalFlowEval,
+        transforms=OpticalFlow,
         meta={
             **_COMMON_META,
             "num_params": 5257536,
@@ -596,7 +596,7 @@ class Raft_Large_Weights(WeightsEnum):
         # Same as CT_SKHT with extra fine-tuning on Kitti
         # Corresponds to the C+T+S+K+H on paper with fine-tuning on Sintel and then on Kitti
         url="https://download.pytorch.org/models/raft_large_C_T_SKHT_K_V2-b5c70766.pth",
-        transforms=OpticalFlowEval,
+        transforms=OpticalFlow,
         meta={
             **_COMMON_META,
             "num_params": 5257536,
@@ -612,7 +612,7 @@ class Raft_Small_Weights(WeightsEnum):
     C_T_V1 = Weights(
         # Chairs + Things, ported from original paper repo (raft-small.pth)
         url="https://download.pytorch.org/models/raft_small_C_T_V1-ad48884c.pth",
-        transforms=OpticalFlowEval,
+        transforms=OpticalFlow,
         meta={
             **_COMMON_META,
             "num_params": 990162,
@@ -626,7 +626,7 @@ class Raft_Small_Weights(WeightsEnum):
     C_T_V2 = Weights(
         # Chairs + Things
         url="https://download.pytorch.org/models/raft_small_C_T_V2-01064c6d.pth",
-        transforms=OpticalFlowEval,
+        transforms=OpticalFlow,
         meta={
             **_COMMON_META,
             "num_params": 990162,
diff --git a/torchvision/models/quantization/googlenet.py b/torchvision/models/quantization/googlenet.py
index befc2299c06..9944e470352 100644
--- a/torchvision/models/quantization/googlenet.py
+++ b/torchvision/models/quantization/googlenet.py
@@ -7,7 +7,7 @@
 from torch import Tensor
 from torch.nn import functional as F
 
-from ...transforms import ImageClassificationEval, InterpolationMode
+from ...transforms._presets import ImageClassification, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -109,7 +109,7 @@ def fuse_model(self, is_qat: Optional[bool] = None) -> None:
 class GoogLeNet_QuantizedWeights(WeightsEnum):
     IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/googlenet_fbgemm-c00238cf.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             "task": "image_classification",
             "architecture": "GoogLeNet",
diff --git a/torchvision/models/quantization/inception.py b/torchvision/models/quantization/inception.py
index 697d99d4027..9a732f79fb7 100644
--- a/torchvision/models/quantization/inception.py
+++ b/torchvision/models/quantization/inception.py
@@ -9,7 +9,7 @@
 from torchvision.models import inception as inception_module
 from torchvision.models.inception import InceptionOutputs, Inception_V3_Weights
 
-from ...transforms import ImageClassificationEval, InterpolationMode
+from ...transforms._presets import ImageClassification, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -175,7 +175,7 @@ def fuse_model(self, is_qat: Optional[bool] = None) -> None:
 class Inception_V3_QuantizedWeights(WeightsEnum):
     IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/inception_v3_google_fbgemm-71447a44.pth",
-        transforms=partial(ImageClassificationEval, crop_size=299, resize_size=342),
+        transforms=partial(ImageClassification, crop_size=299, resize_size=342),
         meta={
             "task": "image_classification",
             "architecture": "InceptionV3",
diff --git a/torchvision/models/quantization/mobilenetv2.py b/torchvision/models/quantization/mobilenetv2.py
index 40f5cb544fd..1def3d24b28 100644
--- a/torchvision/models/quantization/mobilenetv2.py
+++ b/torchvision/models/quantization/mobilenetv2.py
@@ -7,7 +7,7 @@
 from torchvision.models.mobilenetv2 import InvertedResidual, MobileNetV2, MobileNet_V2_Weights
 
 from ...ops.misc import Conv2dNormActivation
-from ...transforms import ImageClassificationEval, InterpolationMode
+from ...transforms._presets import ImageClassification, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -67,7 +67,7 @@ def fuse_model(self, is_qat: Optional[bool] = None) -> None:
 class MobileNet_V2_QuantizedWeights(WeightsEnum):
     IMAGENET1K_QNNPACK_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             "task": "image_classification",
             "architecture": "MobileNetV2",
diff --git a/torchvision/models/quantization/mobilenetv3.py b/torchvision/models/quantization/mobilenetv3.py
index 4b79b7f26ae..4a203ca7095 100644
--- a/torchvision/models/quantization/mobilenetv3.py
+++ b/torchvision/models/quantization/mobilenetv3.py
@@ -6,7 +6,7 @@
 from torch.ao.quantization import QuantStub, DeQuantStub
 
 from ...ops.misc import Conv2dNormActivation, SqueezeExcitation
-from ...transforms import ImageClassificationEval, InterpolationMode
+from ...transforms._presets import ImageClassification, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -157,7 +157,7 @@ def _mobilenet_v3_model(
 class MobileNet_V3_Large_QuantizedWeights(WeightsEnum):
     IMAGENET1K_QNNPACK_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/mobilenet_v3_large_qnnpack-5bcacf28.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             "task": "image_classification",
             "architecture": "MobileNetV3",
diff --git a/torchvision/models/quantization/resnet.py b/torchvision/models/quantization/resnet.py
index 666b1b23163..ab512a7413f 100644
--- a/torchvision/models/quantization/resnet.py
+++ b/torchvision/models/quantization/resnet.py
@@ -13,7 +13,7 @@
     ResNeXt101_32X8D_Weights,
 )
 
-from ...transforms import ImageClassificationEval, InterpolationMode
+from ...transforms._presets import ImageClassification, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -161,7 +161,7 @@ def _resnet(
 class ResNet18_QuantizedWeights(WeightsEnum):
     IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/resnet18_fbgemm_16fa66dd.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -178,7 +178,7 @@ class ResNet18_QuantizedWeights(WeightsEnum):
 class ResNet50_QuantizedWeights(WeightsEnum):
     IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/resnet50_fbgemm_bf931d71.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -191,7 +191,7 @@ class ResNet50_QuantizedWeights(WeightsEnum):
     )
     IMAGENET1K_FBGEMM_V2 = Weights(
         url="https://download.pytorch.org/models/quantized/resnet50_fbgemm-23753f79.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -208,7 +208,7 @@ class ResNet50_QuantizedWeights(WeightsEnum):
 class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
     IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm_09835ccf.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
@@ -221,7 +221,7 @@ class ResNeXt101_32X8D_QuantizedWeights(WeightsEnum):
     )
     IMAGENET1K_FBGEMM_V2 = Weights(
         url="https://download.pytorch.org/models/quantized/resnext101_32x8_fbgemm-ee16d00c.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
diff --git a/torchvision/models/quantization/shufflenetv2.py b/torchvision/models/quantization/shufflenetv2.py
index c5bfe698636..a3a26120479 100644
--- a/torchvision/models/quantization/shufflenetv2.py
+++ b/torchvision/models/quantization/shufflenetv2.py
@@ -6,7 +6,7 @@
 from torch import Tensor
 from torchvision.models import shufflenetv2
 
-from ...transforms import ImageClassificationEval, InterpolationMode
+from ...transforms._presets import ImageClassification, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _IMAGENET_CATEGORIES
 from .._utils import handle_legacy_interface, _ovewrite_named_param
@@ -118,7 +118,7 @@ def _shufflenetv2(
 class ShuffleNet_V2_X0_5_QuantizedWeights(WeightsEnum):
     IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/shufflenetv2_x0.5_fbgemm-00845098.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 1366792,
@@ -133,7 +133,7 @@ class ShuffleNet_V2_X0_5_QuantizedWeights(WeightsEnum):
 class ShuffleNet_V2_X1_0_QuantizedWeights(WeightsEnum):
     IMAGENET1K_FBGEMM_V1 = Weights(
         url="https://download.pytorch.org/models/quantized/shufflenetv2_x1_fbgemm-db332c57.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 2278604,
diff --git a/torchvision/models/regnet.py b/torchvision/models/regnet.py
index 1015c21b858..72093686d84 100644
--- a/torchvision/models/regnet.py
+++ b/torchvision/models/regnet.py
@@ -7,7 +7,7 @@
 from torch import nn, Tensor
 
 from ..ops.misc import Conv2dNormActivation, SqueezeExcitation
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -416,7 +416,7 @@ def _regnet(
 class RegNet_Y_400MF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_400mf-c65dace8.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 4344144,
@@ -427,7 +427,7 @@ class RegNet_Y_400MF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_400mf-e6988f5f.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 4344144,
@@ -442,7 +442,7 @@ class RegNet_Y_400MF_Weights(WeightsEnum):
 class RegNet_Y_800MF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_800mf-1b27b58c.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 6432512,
@@ -453,7 +453,7 @@ class RegNet_Y_800MF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_800mf-58fc7688.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 6432512,
@@ -468,7 +468,7 @@ class RegNet_Y_800MF_Weights(WeightsEnum):
 class RegNet_Y_1_6GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_1_6gf-b11a554e.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 11202430,
@@ -479,7 +479,7 @@ class RegNet_Y_1_6GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_1_6gf-0d7bc02a.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 11202430,
@@ -494,7 +494,7 @@ class RegNet_Y_1_6GF_Weights(WeightsEnum):
 class RegNet_Y_3_2GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_3_2gf-b5a9779c.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 19436338,
@@ -505,7 +505,7 @@ class RegNet_Y_3_2GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_3_2gf-9180c971.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 19436338,
@@ -520,7 +520,7 @@ class RegNet_Y_3_2GF_Weights(WeightsEnum):
 class RegNet_Y_8GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_8gf-d0d0e4a8.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 39381472,
@@ -531,7 +531,7 @@ class RegNet_Y_8GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_8gf-dc2b1b54.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 39381472,
@@ -546,7 +546,7 @@ class RegNet_Y_8GF_Weights(WeightsEnum):
 class RegNet_Y_16GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_16gf-9e6ed7dd.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 83590140,
@@ -557,7 +557,7 @@ class RegNet_Y_16GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_16gf-3e4a00f9.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 83590140,
@@ -572,7 +572,7 @@ class RegNet_Y_16GF_Weights(WeightsEnum):
 class RegNet_Y_32GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_y_32gf-4dee3f7a.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 145046770,
@@ -583,7 +583,7 @@ class RegNet_Y_32GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_y_32gf-8db6d4b5.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 145046770,
@@ -603,7 +603,7 @@ class RegNet_Y_128GF_Weights(WeightsEnum):
 class RegNet_X_400MF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_400mf-adf1edd5.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 5495976,
@@ -614,7 +614,7 @@ class RegNet_X_400MF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_400mf-62229a5f.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 5495976,
@@ -629,7 +629,7 @@ class RegNet_X_400MF_Weights(WeightsEnum):
 class RegNet_X_800MF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_800mf-ad17e45c.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 7259656,
@@ -640,7 +640,7 @@ class RegNet_X_800MF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_800mf-94a99ebd.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 7259656,
@@ -655,7 +655,7 @@ class RegNet_X_800MF_Weights(WeightsEnum):
 class RegNet_X_1_6GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_1_6gf-e3633e7f.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 9190136,
@@ -666,7 +666,7 @@ class RegNet_X_1_6GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_1_6gf-a12f2b72.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 9190136,
@@ -681,7 +681,7 @@ class RegNet_X_1_6GF_Weights(WeightsEnum):
 class RegNet_X_3_2GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_3_2gf-f342aeae.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 15296552,
@@ -692,7 +692,7 @@ class RegNet_X_3_2GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_3_2gf-7071aa85.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 15296552,
@@ -707,7 +707,7 @@ class RegNet_X_3_2GF_Weights(WeightsEnum):
 class RegNet_X_8GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_8gf-03ceed89.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 39572648,
@@ -718,7 +718,7 @@ class RegNet_X_8GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_8gf-2b70d774.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 39572648,
@@ -733,7 +733,7 @@ class RegNet_X_8GF_Weights(WeightsEnum):
 class RegNet_X_16GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_16gf-2007eb11.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 54278536,
@@ -744,7 +744,7 @@ class RegNet_X_16GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_16gf-ba3796d7.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 54278536,
@@ -759,7 +759,7 @@ class RegNet_X_16GF_Weights(WeightsEnum):
 class RegNet_X_32GF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/regnet_x_32gf-9d47f8d0.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 107811560,
@@ -770,7 +770,7 @@ class RegNet_X_32GF_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/regnet_x_32gf-6eb8fdc6.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "num_params": 107811560,
diff --git a/torchvision/models/resnet.py b/torchvision/models/resnet.py
index 159749df006..8f44e553296 100644
--- a/torchvision/models/resnet.py
+++ b/torchvision/models/resnet.py
@@ -5,7 +5,7 @@
 import torch.nn as nn
 from torch import Tensor
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -313,7 +313,7 @@ def _resnet(
 class ResNet18_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet18-f37072fd.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -330,7 +330,7 @@ class ResNet18_Weights(WeightsEnum):
 class ResNet34_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet34-b627a593.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -347,7 +347,7 @@ class ResNet34_Weights(WeightsEnum):
 class ResNet50_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet50-0676ba61.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -360,7 +360,7 @@ class ResNet50_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnet50-11ad3fa6.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -377,7 +377,7 @@ class ResNet50_Weights(WeightsEnum):
 class ResNet101_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet101-63fe2227.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -390,7 +390,7 @@ class ResNet101_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnet101-cd907fc2.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -407,7 +407,7 @@ class ResNet101_Weights(WeightsEnum):
 class ResNet152_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnet152-394f9c45.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -420,7 +420,7 @@ class ResNet152_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnet152-f82ba261.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "ResNet",
@@ -437,7 +437,7 @@ class ResNet152_Weights(WeightsEnum):
 class ResNeXt50_32X4D_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
@@ -450,7 +450,7 @@ class ResNeXt50_32X4D_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnext50_32x4d-1a0047aa.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
@@ -467,7 +467,7 @@ class ResNeXt50_32X4D_Weights(WeightsEnum):
 class ResNeXt101_32X8D_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/resnext101_32x8d-8ba56ff5.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
@@ -480,7 +480,7 @@ class ResNeXt101_32X8D_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/resnext101_32x8d-110c445d.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "ResNeXt",
@@ -497,7 +497,7 @@ class ResNeXt101_32X8D_Weights(WeightsEnum):
 class Wide_ResNet50_2_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/wide_resnet50_2-95faca4d.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "WideResNet",
@@ -510,7 +510,7 @@ class Wide_ResNet50_2_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/wide_resnet50_2-9ba9bcbe.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "WideResNet",
@@ -527,7 +527,7 @@ class Wide_ResNet50_2_Weights(WeightsEnum):
 class Wide_ResNet101_2_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/wide_resnet101_2-32ee1156.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "architecture": "WideResNet",
@@ -540,7 +540,7 @@ class Wide_ResNet101_2_Weights(WeightsEnum):
     )
     IMAGENET1K_V2 = Weights(
         url="https://download.pytorch.org/models/wide_resnet101_2-d733dc28.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=232),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=232),
         meta={
             **_COMMON_META,
             "architecture": "WideResNet",
diff --git a/torchvision/models/segmentation/deeplabv3.py b/torchvision/models/segmentation/deeplabv3.py
index 6e8bf0c398b..41ab34bae07 100644
--- a/torchvision/models/segmentation/deeplabv3.py
+++ b/torchvision/models/segmentation/deeplabv3.py
@@ -5,7 +5,7 @@
 from torch import nn
 from torch.nn import functional as F
 
-from ...transforms import SemanticSegmentationEval, InterpolationMode
+from ...transforms._presets import SemanticSegmentation, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_CATEGORIES
 from .._utils import IntermediateLayerGetter, handle_legacy_interface, _ovewrite_value_param
@@ -140,7 +140,7 @@ def _deeplabv3_resnet(
 class DeepLabV3_ResNet50_Weights(WeightsEnum):
     COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/deeplabv3_resnet50_coco-cd0a2569.pth",
-        transforms=partial(SemanticSegmentationEval, resize_size=520),
+        transforms=partial(SemanticSegmentation, resize_size=520),
         meta={
             **_COMMON_META,
             "num_params": 42004074,
@@ -155,7 +155,7 @@ class DeepLabV3_ResNet50_Weights(WeightsEnum):
 class DeepLabV3_ResNet101_Weights(WeightsEnum):
     COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/deeplabv3_resnet101_coco-586e9e4e.pth",
-        transforms=partial(SemanticSegmentationEval, resize_size=520),
+        transforms=partial(SemanticSegmentation, resize_size=520),
         meta={
             **_COMMON_META,
             "num_params": 60996202,
@@ -170,7 +170,7 @@ class DeepLabV3_ResNet101_Weights(WeightsEnum):
 class DeepLabV3_MobileNet_V3_Large_Weights(WeightsEnum):
     COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pth",
-        transforms=partial(SemanticSegmentationEval, resize_size=520),
+        transforms=partial(SemanticSegmentation, resize_size=520),
         meta={
             **_COMMON_META,
             "num_params": 11029328,
diff --git a/torchvision/models/segmentation/fcn.py b/torchvision/models/segmentation/fcn.py
index 5a3ca1f654f..6a760be36dc 100644
--- a/torchvision/models/segmentation/fcn.py
+++ b/torchvision/models/segmentation/fcn.py
@@ -3,7 +3,7 @@
 
 from torch import nn
 
-from ...transforms import SemanticSegmentationEval, InterpolationMode
+from ...transforms._presets import SemanticSegmentation, InterpolationMode
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_CATEGORIES
 from .._utils import IntermediateLayerGetter, handle_legacy_interface, _ovewrite_value_param
@@ -59,7 +59,7 @@ def __init__(self, in_channels: int, channels: int) -> None:
 class FCN_ResNet50_Weights(WeightsEnum):
     COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/fcn_resnet50_coco-1167a1af.pth",
-        transforms=partial(SemanticSegmentationEval, resize_size=520),
+        transforms=partial(SemanticSegmentation, resize_size=520),
         meta={
             **_COMMON_META,
             "num_params": 35322218,
@@ -74,7 +74,7 @@ class FCN_ResNet50_Weights(WeightsEnum):
 class FCN_ResNet101_Weights(WeightsEnum):
     COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/fcn_resnet101_coco-7ecb50ca.pth",
-        transforms=partial(SemanticSegmentationEval, resize_size=520),
+        transforms=partial(SemanticSegmentation, resize_size=520),
         meta={
             **_COMMON_META,
             "num_params": 54314346,
diff --git a/torchvision/models/segmentation/lraspp.py b/torchvision/models/segmentation/lraspp.py
index d1fe15a350d..33684526c6b 100644
--- a/torchvision/models/segmentation/lraspp.py
+++ b/torchvision/models/segmentation/lraspp.py
@@ -5,7 +5,7 @@
 from torch import nn, Tensor
 from torch.nn import functional as F
 
-from ...transforms import SemanticSegmentationEval, InterpolationMode
+from ...transforms._presets import SemanticSegmentation, InterpolationMode
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _VOC_CATEGORIES
@@ -96,7 +96,7 @@ def _lraspp_mobilenetv3(backbone: MobileNetV3, num_classes: int) -> LRASPP:
 class LRASPP_MobileNet_V3_Large_Weights(WeightsEnum):
     COCO_WITH_VOC_LABELS_V1 = Weights(
         url="https://download.pytorch.org/models/lraspp_mobilenet_v3_large-d234d4ea.pth",
-        transforms=partial(SemanticSegmentationEval, resize_size=520),
+        transforms=partial(SemanticSegmentation, resize_size=520),
         meta={
             "task": "image_semantic_segmentation",
             "architecture": "LRASPP",
diff --git a/torchvision/models/shufflenetv2.py b/torchvision/models/shufflenetv2.py
index b38c0ac2974..e988b819078 100644
--- a/torchvision/models/shufflenetv2.py
+++ b/torchvision/models/shufflenetv2.py
@@ -5,7 +5,7 @@
 import torch.nn as nn
 from torch import Tensor
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -198,7 +198,7 @@ def _shufflenetv2(
 class ShuffleNet_V2_X0_5_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 1366792,
@@ -212,7 +212,7 @@ class ShuffleNet_V2_X0_5_Weights(WeightsEnum):
 class ShuffleNet_V2_X1_0_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 2278604,
diff --git a/torchvision/models/squeezenet.py b/torchvision/models/squeezenet.py
index d495b3148e5..bde8b5efcfd 100644
--- a/torchvision/models/squeezenet.py
+++ b/torchvision/models/squeezenet.py
@@ -5,7 +5,7 @@
 import torch.nn as nn
 import torch.nn.init as init
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -128,7 +128,7 @@ def _squeezenet(
 class SqueezeNet1_0_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/squeezenet1_0-b66bff10.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "min_size": (21, 21),
@@ -143,7 +143,7 @@ class SqueezeNet1_0_Weights(WeightsEnum):
 class SqueezeNet1_1_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/squeezenet1_1-b8a52dc0.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "min_size": (17, 17),
diff --git a/torchvision/models/vgg.py b/torchvision/models/vgg.py
index 27325c9016c..93bfd5e6ba3 100644
--- a/torchvision/models/vgg.py
+++ b/torchvision/models/vgg.py
@@ -4,7 +4,7 @@
 import torch
 import torch.nn as nn
 
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -120,7 +120,7 @@ def _vgg(cfg: str, batch_norm: bool, weights: Optional[WeightsEnum], progress: b
 class VGG11_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg11-8a719046.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 132863336,
@@ -134,7 +134,7 @@ class VGG11_Weights(WeightsEnum):
 class VGG11_BN_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg11_bn-6002323d.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 132868840,
@@ -148,7 +148,7 @@ class VGG11_BN_Weights(WeightsEnum):
 class VGG13_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg13-19584684.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 133047848,
@@ -162,7 +162,7 @@ class VGG13_Weights(WeightsEnum):
 class VGG13_BN_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg13_bn-abd245e5.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 133053736,
@@ -176,7 +176,7 @@ class VGG13_BN_Weights(WeightsEnum):
 class VGG16_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg16-397923af.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 138357544,
@@ -190,7 +190,7 @@ class VGG16_Weights(WeightsEnum):
     IMAGENET1K_FEATURES = Weights(
         url="https://download.pytorch.org/models/vgg16_features-amdegroot-88682ab5.pth",
         transforms=partial(
-            ImageClassificationEval,
+            ImageClassification,
             crop_size=224,
             mean=(0.48235, 0.45882, 0.40784),
             std=(1.0 / 255.0, 1.0 / 255.0, 1.0 / 255.0),
@@ -210,7 +210,7 @@ class VGG16_Weights(WeightsEnum):
 class VGG16_BN_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg16_bn-6c64b313.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 138365992,
@@ -224,7 +224,7 @@ class VGG16_BN_Weights(WeightsEnum):
 class VGG19_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg19-dcbb9e9d.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 143667240,
@@ -238,7 +238,7 @@ class VGG19_Weights(WeightsEnum):
 class VGG19_BN_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vgg19_bn-c79401a0.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 143678248,
diff --git a/torchvision/models/video/resnet.py b/torchvision/models/video/resnet.py
index a6b779d10f1..618ddb96ba2 100644
--- a/torchvision/models/video/resnet.py
+++ b/torchvision/models/video/resnet.py
@@ -4,7 +4,7 @@
 import torch.nn as nn
 from torch import Tensor
 
-from ...transforms import VideoClassificationEval, InterpolationMode
+from ...transforms._presets import VideoClassification, InterpolationMode
 from ...utils import _log_api_usage_once
 from .._api import WeightsEnum, Weights
 from .._meta import _KINETICS400_CATEGORIES
@@ -322,7 +322,7 @@ def _video_resnet(
 class R3D_18_Weights(WeightsEnum):
     KINETICS400_V1 = Weights(
         url="https://download.pytorch.org/models/r3d_18-b3b3357e.pth",
-        transforms=partial(VideoClassificationEval, crop_size=(112, 112), resize_size=(128, 171)),
+        transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
             **_COMMON_META,
             "architecture": "R3D",
@@ -337,7 +337,7 @@ class R3D_18_Weights(WeightsEnum):
 class MC3_18_Weights(WeightsEnum):
     KINETICS400_V1 = Weights(
         url="https://download.pytorch.org/models/mc3_18-a90a0ba3.pth",
-        transforms=partial(VideoClassificationEval, crop_size=(112, 112), resize_size=(128, 171)),
+        transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
             **_COMMON_META,
             "architecture": "MC3",
@@ -352,7 +352,7 @@ class MC3_18_Weights(WeightsEnum):
 class R2Plus1D_18_Weights(WeightsEnum):
     KINETICS400_V1 = Weights(
         url="https://download.pytorch.org/models/r2plus1d_18-91a641e6.pth",
-        transforms=partial(VideoClassificationEval, crop_size=(112, 112), resize_size=(128, 171)),
+        transforms=partial(VideoClassification, crop_size=(112, 112), resize_size=(128, 171)),
         meta={
             **_COMMON_META,
             "architecture": "R(2+1)D",
diff --git a/torchvision/models/vision_transformer.py b/torchvision/models/vision_transformer.py
index 801e7adc981..fb34cf3c8e1 100644
--- a/torchvision/models/vision_transformer.py
+++ b/torchvision/models/vision_transformer.py
@@ -7,7 +7,7 @@
 import torch.nn as nn
 
 from ..ops.misc import Conv2dNormActivation
-from ..transforms import ImageClassificationEval, InterpolationMode
+from ..transforms._presets import ImageClassification, InterpolationMode
 from ..utils import _log_api_usage_once
 from ._api import WeightsEnum, Weights
 from ._meta import _IMAGENET_CATEGORIES
@@ -317,7 +317,7 @@ def _vision_transformer(
 class ViT_B_16_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vit_b_16-c867db91.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 86567656,
@@ -334,7 +334,7 @@ class ViT_B_16_Weights(WeightsEnum):
 class ViT_B_32_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vit_b_32-d86f8d99.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 88224232,
@@ -351,7 +351,7 @@ class ViT_B_32_Weights(WeightsEnum):
 class ViT_L_16_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vit_l_16-852ce7e3.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224, resize_size=242),
+        transforms=partial(ImageClassification, crop_size=224, resize_size=242),
         meta={
             **_COMMON_META,
             "num_params": 304326632,
@@ -368,7 +368,7 @@ class ViT_L_16_Weights(WeightsEnum):
 class ViT_L_32_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
         url="https://download.pytorch.org/models/vit_l_32-c7638314.pth",
-        transforms=partial(ImageClassificationEval, crop_size=224),
+        transforms=partial(ImageClassification, crop_size=224),
         meta={
             **_COMMON_META,
             "num_params": 306535400,
diff --git a/torchvision/transforms/__init__.py b/torchvision/transforms/__init__.py
index 94ec34ebe98..77680a14f0d 100644
--- a/torchvision/transforms/__init__.py
+++ b/torchvision/transforms/__init__.py
@@ -1,9 +1,2 @@
 from .transforms import *
 from .autoaugment import *
-from ._presets import (
-    ObjectDetectionEval,
-    ImageClassificationEval,
-    SemanticSegmentationEval,
-    VideoClassificationEval,
-    OpticalFlowEval,
-)
diff --git a/torchvision/transforms/_presets.py b/torchvision/transforms/_presets.py
index 1776d876ccb..0bfb1cf9b38 100644
--- a/torchvision/transforms/_presets.py
+++ b/torchvision/transforms/_presets.py
@@ -1,4 +1,8 @@
-from typing import Dict, Optional, Tuple
+"""
+This file is part of the private API. Please do not use directly these classes as they will be modified on
+future versions without warning. The classes should be accessed only via the transforms argument of Weights.
+"""
+from typing import Optional, Tuple
 
 import torch
 from torch import Tensor, nn
@@ -7,24 +11,22 @@
 
 
 __all__ = [
-    "ObjectDetectionEval",
-    "ImageClassificationEval",
-    "VideoClassificationEval",
-    "SemanticSegmentationEval",
-    "OpticalFlowEval",
+    "ObjectDetection",
+    "ImageClassification",
+    "VideoClassification",
+    "SemanticSegmentation",
+    "OpticalFlow",
 ]
 
 
-class ObjectDetectionEval(nn.Module):
-    def forward(
-        self, img: Tensor, target: Optional[Dict[str, Tensor]] = None
-    ) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]:
+class ObjectDetection(nn.Module):
+    def forward(self, img: Tensor) -> Tensor:
         if not isinstance(img, Tensor):
             img = F.pil_to_tensor(img)
-        return F.convert_image_dtype(img, torch.float), target
+        return F.convert_image_dtype(img, torch.float)
 
 
-class ImageClassificationEval(nn.Module):
+class ImageClassification(nn.Module):
     def __init__(
         self,
         crop_size: int,
@@ -50,7 +52,7 @@ def forward(self, img: Tensor) -> Tensor:
         return img
 
 
-class VideoClassificationEval(nn.Module):
+class VideoClassification(nn.Module):
     def __init__(
         self,
         crop_size: Tuple[int, int],
@@ -67,55 +69,59 @@ def __init__(
         self._interpolation = interpolation
 
     def forward(self, vid: Tensor) -> Tensor:
-        vid = vid.permute(0, 3, 1, 2)  # (T, H, W, C) => (T, C, H, W)
+        need_squeeze = False
+        if vid.ndim < 5:
+            vid = vid.unsqueeze(dim=0)
+            need_squeeze = True
+
+        vid = vid.permute(0, 1, 4, 2, 3)  # (N, T, H, W, C) => (N, T, C, H, W)
+        N, T, C, H, W = vid.shape
+        vid = vid.view(-1, C, H, W)
         vid = F.resize(vid, self._size, interpolation=self._interpolation)
         vid = F.center_crop(vid, self._crop_size)
         vid = F.convert_image_dtype(vid, torch.float)
         vid = F.normalize(vid, mean=self._mean, std=self._std)
-        return vid.permute(1, 0, 2, 3)  # (T, C, H, W) => (C, T, H, W)
+        vid = vid.view(N, T, C, H, W)
+        vid = vid.permute(0, 2, 1, 3, 4)  # (N, T, C, H, W) => (N, C, T, H, W)
+
+        if need_squeeze:
+            vid = vid.squeeze(dim=0)
+        return vid
 
 
-class SemanticSegmentationEval(nn.Module):
+class SemanticSegmentation(nn.Module):
     def __init__(
         self,
         resize_size: Optional[int],
         mean: Tuple[float, ...] = (0.485, 0.456, 0.406),
         std: Tuple[float, ...] = (0.229, 0.224, 0.225),
         interpolation: InterpolationMode = InterpolationMode.BILINEAR,
-        interpolation_target: InterpolationMode = InterpolationMode.NEAREST,
     ) -> None:
         super().__init__()
         self._size = [resize_size] if resize_size is not None else None
         self._mean = list(mean)
         self._std = list(std)
         self._interpolation = interpolation
-        self._interpolation_target = interpolation_target
 
-    def forward(self, img: Tensor, target: Optional[Tensor] = None) -> Tuple[Tensor, Optional[Tensor]]:
+    def forward(self, img: Tensor) -> Tensor:
         if isinstance(self._size, list):
             img = F.resize(img, self._size, interpolation=self._interpolation)
         if not isinstance(img, Tensor):
             img = F.pil_to_tensor(img)
         img = F.convert_image_dtype(img, torch.float)
         img = F.normalize(img, mean=self._mean, std=self._std)
-        if target:
-            if isinstance(self._size, list):
-                target = F.resize(target, self._size, interpolation=self._interpolation_target)
-            if not isinstance(target, Tensor):
-                target = F.pil_to_tensor(target)
-            target = target.squeeze(0).to(torch.int64)
-        return img, target
-
+        return img
 
-class OpticalFlowEval(nn.Module):
-    def forward(
-        self, img1: Tensor, img2: Tensor, flow: Optional[Tensor] = None, valid_flow_mask: Optional[Tensor] = None
-    ) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]:
 
-        img1, img2, flow, valid_flow_mask = self._pil_or_numpy_to_tensor(img1, img2, flow, valid_flow_mask)
+class OpticalFlow(nn.Module):
+    def forward(self, img1: Tensor, img2: Tensor) -> Tuple[Tensor, Tensor]:
+        if not isinstance(img1, Tensor):
+            img1 = F.pil_to_tensor(img1)
+        if not isinstance(img2, Tensor):
+            img2 = F.pil_to_tensor(img2)
 
-        img1 = F.convert_image_dtype(img1, torch.float32)
-        img2 = F.convert_image_dtype(img2, torch.float32)
+        img1 = F.convert_image_dtype(img1, torch.float)
+        img2 = F.convert_image_dtype(img2, torch.float)
 
         # map [0, 1] into [-1, 1]
         img1 = F.normalize(img1, mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
@@ -124,19 +130,4 @@ def forward(
         img1 = img1.contiguous()
         img2 = img2.contiguous()
 
-        return img1, img2, flow, valid_flow_mask
-
-    def _pil_or_numpy_to_tensor(
-        self, img1: Tensor, img2: Tensor, flow: Optional[Tensor], valid_flow_mask: Optional[Tensor]
-    ) -> Tuple[Tensor, Tensor, Optional[Tensor], Optional[Tensor]]:
-        if not isinstance(img1, Tensor):
-            img1 = F.pil_to_tensor(img1)
-        if not isinstance(img2, Tensor):
-            img2 = F.pil_to_tensor(img2)
-
-        if flow is not None and not isinstance(flow, Tensor):
-            flow = torch.from_numpy(flow)
-        if valid_flow_mask is not None and not isinstance(valid_flow_mask, Tensor):
-            valid_flow_mask = torch.from_numpy(valid_flow_mask)
-
-        return img1, img2, flow, valid_flow_mask
+        return img1, img2