Vit Pytorch

$ pip install vit-pytorch

import torch
from vit_pytorch import ViT

v = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(1, 3, 256, 256)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch import SimpleViT

v = SimpleViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048
)

img = torch.randn(1, 3, 256, 256)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch.na_vit import NaViT

v = NaViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1,
    token_dropout_prob = 0.1  # token dropout of 10% (keep 90% of tokens)
)

# 5 images of different resolutions - List[List[Tensor]]

# for now, you'll have to correctly place images in same batch element as to not exceed maximum allowed sequence length for self-attention w/ masking

images = [
    [torch.randn(3, 256, 256), torch.randn(3, 128, 128)],
    [torch.randn(3, 128, 256), torch.randn(3, 256, 128)],
    [torch.randn(3, 64, 256)]
]

preds = v(images) # (5, 1000) - 5, because 5 images of different resolution above

images = [
    torch.randn(3, 256, 256),
    torch.randn(3, 128, 128),
    torch.randn(3, 128, 256),
    torch.randn(3, 256, 128),
    torch.randn(3, 64, 256)
]

preds = v(
    images,
    group_images = True,
    group_max_seq_len = 64
) # (5, 1000)

import torch
from vit_pytorch.na_vit_nested_tensor import NaViT

v = NaViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.,
    emb_dropout = 0.,
    token_dropout_prob = 0.1
)

# 5 images of different resolutions - List[Tensor]

images = [
    torch.randn(3, 256, 256), torch.randn(3, 128, 128),
    torch.randn(3, 128, 256), torch.randn(3, 256, 128),
    torch.randn(3, 64, 256)
]

preds = v(images)

assert preds.shape == (5, 1000)

import torch
from torchvision.models import resnet50

from vit_pytorch.distill import DistillableViT, DistillWrapper

teacher = resnet50(pretrained = True)

v = DistillableViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

distiller = DistillWrapper(
    student = v,
    teacher = teacher,
    temperature = 3,           # temperature of distillation
    alpha = 0.5,               # trade between main loss and distillation loss
    hard = False               # whether to use soft or hard distillation
)

img = torch.randn(2, 3, 256, 256)
labels = torch.randint(0, 1000, (2,))

loss = distiller(img, labels)
loss.backward()

# after lots of training above ...

pred = v(img) # (2, 1000)

v = v.to_vit()
type(v) # 

import torch
from vit_pytorch.deepvit import DeepViT

v = DeepViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(1, 3, 256, 256)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch.cait import CaiT

v = CaiT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 12,             # depth of transformer for patch to patch attention only
    cls_depth = 2,          # depth of cross attention of CLS tokens to patch
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1,
    layer_dropout = 0.05    # randomly dropout 5% of the layers
)

img = torch.randn(1, 3, 256, 256)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch.t2t import T2TViT

v = T2TViT(
    dim = 512,
    image_size = 224,
    depth = 5,
    heads = 8,
    mlp_dim = 512,
    num_classes = 1000,
    t2t_layers = ((7, 4), (3, 2), (3, 2)) # tuples of the kernel size and stride of each consecutive layers of the initial token to token module
)

img = torch.randn(1, 3, 224, 224)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch.cct import CCT

cct = CCT(
    img_size = (224, 448),
    embedding_dim = 384,
    n_conv_layers = 2,
    kernel_size = 7,
    stride = 2,
    padding = 3,
    pooling_kernel_size = 3,
    pooling_stride = 2,
    pooling_padding = 1,
    num_layers = 14,
    num_heads = 6,
    mlp_ratio = 3.,
    num_classes = 1000,
    positional_embedding = 'learnable', # ['sine', 'learnable', 'none']
)

img = torch.randn(1, 3, 224, 448)
pred = cct(img) # (1, 1000)

import torch
from vit_pytorch.cct import cct_14

cct = cct_14(
    img_size = 224,
    n_conv_layers = 1,
    kernel_size = 7,
    stride = 2,
    padding = 3,
    pooling_kernel_size = 3,
    pooling_stride = 2,
    pooling_padding = 1,
    num_classes = 1000,
    positional_embedding = 'learnable', # ['sine', 'learnable', 'none']
)

import torch
from vit_pytorch.cross_vit import CrossViT

v = CrossViT(
    image_size = 256,
    num_classes = 1000,
    depth = 4,               # number of multi-scale encoding blocks
    sm_dim = 192,            # high res dimension
    sm_patch_size = 16,      # high res patch size (should be smaller than lg_patch_size)
    sm_enc_depth = 2,        # high res depth
    sm_enc_heads = 8,        # high res heads
    sm_enc_mlp_dim = 2048,   # high res feedforward dimension
    lg_dim = 384,            # low res dimension
    lg_patch_size = 64,      # low res patch size
    lg_enc_depth = 3,        # low res depth
    lg_enc_heads = 8,        # low res heads
    lg_enc_mlp_dim = 2048,   # low res feedforward dimensions
    cross_attn_depth = 2,    # cross attention rounds
    cross_attn_heads = 8,    # cross attention heads
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(1, 3, 256, 256)

pred = v(img) # (1, 1000)

import torch
from vit_pytorch.pit import PiT

v = PiT(
    image_size = 224,
    patch_size = 14,
    dim = 256,
    num_classes = 1000,
    depth = (3, 3, 3),     # list of depths, indicating the number of rounds of each stage before a downsample
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

# forward pass now returns predictions and the attention maps

img = torch.randn(1, 3, 224, 224)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch.levit import LeViT

levit = LeViT(
    image_size = 224,
    num_classes = 1000,
    stages = 3,             # number of stages
    dim = (256, 384, 512),  # dimensions at each stage
    depth = 4,              # transformer of depth 4 at each stage
    heads = (4, 6, 8),      # heads at each stage
    mlp_mult = 2,
    dropout = 0.1
)

img = torch.randn(1, 3, 224, 224)

levit(img) # (1, 1000)

import torch
from vit_pytorch.cvt import CvT

v = CvT(
    num_classes = 1000,
    s1_emb_dim = 64,        # stage 1 - dimension
    s1_emb_kernel = 7,      # stage 1 - conv kernel
    s1_emb_stride = 4,      # stage 1 - conv stride
    s1_proj_kernel = 3,     # stage 1 - attention ds-conv kernel size
    s1_kv_proj_stride = 2,  # stage 1 - attention key / value projection stride
    s1_heads = 1,           # stage 1 - heads
    s1_depth = 1,           # stage 1 - depth
    s1_mlp_mult = 4,        # stage 1 - feedforward expansion factor
    s2_emb_dim = 192,       # stage 2 - (same as above)
    s2_emb_kernel = 3,
    s2_emb_stride = 2,
    s2_proj_kernel = 3,
    s2_kv_proj_stride = 2,
    s2_heads = 3,
    s2_depth = 2,
    s2_mlp_mult = 4,
    s3_emb_dim = 384,       # stage 3 - (same as above)
    s3_emb_kernel = 3,
    s3_emb_stride = 2,
    s3_proj_kernel = 3,
    s3_kv_proj_stride = 2,
    s3_heads = 4,
    s3_depth = 10,
    s3_mlp_mult = 4,
    dropout = 0.
)

img = torch.randn(1, 3, 224, 224)

pred = v(img) # (1, 1000)

import torch
from vit_pytorch.twins_svt import TwinsSVT

model = TwinsSVT(
    num_classes = 1000,       # number of output classes
    s1_emb_dim = 64,          # stage 1 - patch embedding projected dimension
    s1_patch_size = 4,        # stage 1 - patch size for patch embedding
    s1_local_patch_size = 7,  # stage 1 - patch size for local attention
    s1_global_k = 7,          # stage 1 - global attention key / value reduction factor, defaults to 7 as specified in paper
    s1_depth = 1,             # stage 1 - number of transformer blocks (local attn -> ff -> global attn -> ff)
    s2_emb_dim = 128,         # stage 2 (same as above)
    s2_patch_size = 2,
    s2_local_patch_size = 7,
    s2_global_k = 7,
    s2_depth = 1,
    s3_emb_dim = 256,         # stage 3 (same as above)
    s3_patch_size = 2,
    s3_local_patch_size = 7,
    s3_global_k = 7,
    s3_depth = 5,
    s4_emb_dim = 512,         # stage 4 (same as above)
    s4_patch_size = 2,
    s4_local_patch_size = 7,
    s4_global_k = 7,
    s4_depth = 4,
    peg_kernel_size = 3,      # positional encoding generator kernel size
    dropout = 0.              # dropout
)

img = torch.randn(1, 3, 224, 224)

pred = model(img) # (1, 1000)

import torch
from vit_pytorch.regionvit import RegionViT

model = RegionViT(
    dim = (64, 128, 256, 512),      # tuple of size 4, indicating dimension at each stage
    depth = (2, 2, 8, 2),           # depth of the region to local transformer at each stage
    window_size = 7,                # window size, which should be either 7 or 14
    num_classes = 1000,             # number of output classes
    tokenize_local_3_conv = False,  # whether to use a 3 layer convolution to encode the local tokens from the image. the paper uses this for the smaller models, but uses only 1 conv (set to False) for the larger models
    use_peg = False,                # whether to use positional generating module. they used this for object detection for a boost in performance
)

img = torch.randn(1, 3, 224, 224)

pred = model(img) # (1, 1000)

import torch
from vit_pytorch.crossformer import CrossFormer

model = CrossFormer(
    num_classes = 1000,                # number of output classes
    dim = (64, 128, 256, 512),         # dimension at each stage
    depth = (2, 2, 8, 2),              # depth of transformer at each stage
    global_window_size = (8, 4, 2, 1), # global window sizes at each stage
    local_window_size = 7,             # local window size (can be customized for each stage, but in paper, held constant at 7 for all stages)
)

img = torch.randn(1, 3, 224, 224)

pred = model(img) # (1, 1000)

import torch
from vit_pytorch.scalable_vit import ScalableViT

model = ScalableViT(
    num_classes = 1000,
    dim = 64,                               # starting model dimension. at every stage, dimension is doubled
    heads = (2, 4, 8, 16),                  # number of attention heads at each stage
    depth = (2, 2, 20, 2),                  # number of transformer blocks at each stage
    ssa_dim_key = (40, 40, 40, 32),         # the dimension of the attention keys (and queries) for SSA. in the paper, they represented this as a scale factor on the base dimension per key (ssa_dim_key / dim_key)
    reduction_factor = (8, 4, 2, 1),        # downsampling of the key / values in SSA. in the paper, this was represented as (reduction_factor ** -2)
    window_size = (64, 32, None, None),     # window size of the IWSA at each stage. None means no windowing needed
    dropout = 0.1,                          # attention and feedforward dropout
)

img = torch.randn(1, 3, 256, 256)

preds = model(img) # (1, 1000)

import torch
from vit_pytorch.sep_vit import SepViT

v = SepViT(
    num_classes = 1000,
    dim = 32,               # dimensions of first stage, which doubles every stage (32, 64, 128, 256) for SepViT-Lite
    dim_head = 32,          # attention head dimension
    heads = (1, 2, 4, 8),   # number of heads per stage
    depth = (1, 2, 6, 2),   # number of transformer blocks per stage
    window_size = 7,        # window size of DSS Attention block
    dropout = 0.1           # dropout
)

img = torch.randn(1, 3, 224, 224)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch.max_vit import MaxViT

v = MaxViT(
    num_classes = 1000,
    dim_conv_stem = 64,               # dimension of the convolutional stem, would default to dimension of first layer if not specified
    dim = 96,                         # dimension of first layer, doubles every layer
    dim_head = 32,                    # dimension of attention heads, kept at 32 in paper
    depth = (2, 2, 5, 2),             # number of MaxViT blocks per stage, which consists of MBConv, block-like attention, grid-like attention
    window_size = 7,                  # window size for block and grids
    mbconv_expansion_rate = 4,        # expansion rate of MBConv
    mbconv_shrinkage_rate = 0.25,     # shrinkage rate of squeeze-excitation in MBConv
    dropout = 0.1                     # dropout
)

img = torch.randn(2, 3, 224, 224)

preds = v(img) # (2, 1000)

import torch
from vit_pytorch.nest import NesT

nest = NesT(
    image_size = 224,
    patch_size = 4,
    dim = 96,
    heads = 3,
    num_hierarchies = 3,        # number of hierarchies
    block_repeats = (2, 2, 8),  # the number of transformer blocks at each hierarchy, starting from the bottom
    num_classes = 1000
)

img = torch.randn(1, 3, 224, 224)

pred = nest(img) # (1, 1000)

import torch
from vit_pytorch.mobile_vit import MobileViT

mbvit_xs = MobileViT(
    image_size = (256, 256),
    dims = [96, 120, 144],
    channels = [16, 32, 48, 48, 64, 64, 80, 80, 96, 96, 384],
    num_classes = 1000
)

img = torch.randn(1, 3, 256, 256)

pred = mbvit_xs(img) # (1, 1000)

import torch
from vit_pytorch.xcit import XCiT

v = XCiT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 12,                     # depth of xcit transformer
    cls_depth = 2,                  # depth of cross attention of CLS tokens to patch, attention pool at end
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1,
    layer_dropout = 0.05,           # randomly dropout 5% of the layers
    local_patch_kernel_size = 3     # kernel size of the local patch interaction module (depthwise convs)
)

img = torch.randn(1, 3, 256, 256)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch import ViT
from vit_pytorch.simmim import SimMIM

v = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048
)

mim = SimMIM(
    encoder = v,
    masking_ratio = 0.5  # they found 50% to yield the best results
)

images = torch.randn(8, 3, 256, 256)

loss = mim(images)
loss.backward()

# that's all!
# do the above in a for loop many times with a lot of images and your vision transformer will learn

torch.save(v.state_dict(), './trained-vit.pt')

import torch
from vit_pytorch import ViT, MAE

v = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048
)

mae = MAE(
    encoder = v,
    masking_ratio = 0.75,   # the paper recommended 75% masked patches
    decoder_dim = 512,      # paper showed good results with just 512
    decoder_depth = 6       # anywhere from 1 to 8
)

images = torch.randn(8, 3, 256, 256)

loss = mae(images)
loss.backward()

# that's all!
# do the above in a for loop many times with a lot of images and your vision transformer will learn

# save your improved vision transformer
torch.save(v.state_dict(), './trained-vit.pt')

import torch
from vit_pytorch import ViT
from vit_pytorch.mpp import MPP

model = ViT(
    image_size=256,
    patch_size=32,
    num_classes=1000,
    dim=1024,
    depth=6,
    heads=8,
    mlp_dim=2048,
    dropout=0.1,
    emb_dropout=0.1
)

mpp_trainer = MPP(
    transformer=model,
    patch_size=32,
    dim=1024,
    mask_prob=0.15,          # probability of using token in masked prediction task
    random_patch_prob=0.30,  # probability of randomly replacing a token being used for mpp
    replace_prob=0.50,       # probability of replacing a token being used for mpp with the mask token
)

opt = torch.optim.Adam(mpp_trainer.parameters(), lr=3e-4)

def sample_unlabelled_images():
    return torch.FloatTensor(20, 3, 256, 256).uniform_(0., 1.)

for _ in range(100):
    images = sample_unlabelled_images()
    loss = mpp_trainer(images)
    opt.zero_grad()
    loss.backward()
    opt.step()

# save your improved network
torch.save(model.state_dict(), './pretrained-net.pt')

import torch
from vit_pytorch.mp3 import ViT, MP3

v = ViT(
    num_classes = 1000,
    image_size = 256,
    patch_size = 8,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048,
    dropout = 0.1,
)

mp3 = MP3(
    vit = v,
    masking_ratio = 0.75
)

images = torch.randn(8, 3, 256, 256)

loss = mp3(images)
loss.backward()

# that's all!
# do the above in a for loop many times with a lot of images and your vision transformer will learn

# save your improved vision transformer
torch.save(v.state_dict(), './trained-vit.pt')

import torch
from vit_pytorch.ats_vit import ViT

v = ViT(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    max_tokens_per_depth = (256, 128, 64, 32, 16, 8), # a tuple that denotes the maximum number of tokens that any given layer should have. if the layer has greater than this amount, it will undergo adaptive token sampling
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(4, 3, 256, 256)

preds = v(img) # (4, 1000)

# you can also get a list of the final sampled patch ids
# a value of -1 denotes padding

preds, token_ids = v(img, return_sampled_token_ids = True) # (4, 1000), (4, <=8)

import torch
from vit_pytorch.vit_with_patch_merger import ViT

v = ViT(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 1024,
    depth = 12,
    heads = 8,
    patch_merge_layer = 6,        # at which transformer layer to do patch merging
    patch_merge_num_tokens = 8,   # the output number of tokens from the patch merge
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(4, 3, 256, 256)

preds = v(img) # (4, 1000)

import torch
from vit_pytorch.vit_with_patch_merger import PatchMerger

merger = PatchMerger(
    dim = 1024,
    num_tokens_out = 8   # output number of tokens
)

features = torch.randn(4, 256, 1024) # (batch, num tokens, dimension)

out = merger(features) # (4, 8, 1024)

import torch
from vit_pytorch.vit_for_small_dataset import ViT

v = ViT(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(4, 3, 256, 256)

preds = v(img) # (1, 1000)

import torch
from vit_pytorch.vit_for_small_dataset import SPT

spt = SPT(
    dim = 1024,
    patch_size = 16,
    channels = 3
)

img = torch.randn(4, 3, 256, 256)

tokens = spt(img) # (4, 256, 1024)

import torch
from vit_pytorch.vit_3d import ViT

v = ViT(
    image_size = 128,          # image size
    frames = 16,               # number of frames
    image_patch_size = 16,     # image patch size
    frame_patch_size = 2,      # frame patch size
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

video = torch.randn(4, 3, 16, 128, 128) # (batch, channels, frames, height, width)

preds = v(video) # (4, 1000)

import torch
from vit_pytorch.simple_vit_3d import SimpleViT

v = SimpleViT(
    image_size = 128,          # image size
    frames = 16,               # number of frames
    image_patch_size = 16,     # image patch size
    frame_patch_size = 2,      # frame patch size
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048
)

video = torch.randn(4, 3, 16, 128, 128) # (batch, channels, frames, height, width)

preds = v(video) # (4, 1000)

import torch
from vit_pytorch.cct_3d import CCT

cct = CCT(
    img_size = 224,
    num_frames = 8,
    embedding_dim = 384,
    n_conv_layers = 2,
    frame_kernel_size = 3,
    kernel_size = 7,
    stride = 2,
    padding = 3,
    pooling_kernel_size = 3,
    pooling_stride = 2,
    pooling_padding = 1,
    num_layers = 14,
    num_heads = 6,
    mlp_ratio = 3.,
    num_classes = 1000,
    positional_embedding = 'learnable'
)

video = torch.randn(1, 3, 8, 224, 224) # (batch, channels, frames, height, width)
pred = cct(video)

import torch
from vit_pytorch.vivit import ViT

v = ViT(
    image_size = 128,          # image size
    frames = 16,               # number of frames
    image_patch_size = 16,     # image patch size
    frame_patch_size = 2,      # frame patch size
    num_classes = 1000,
    dim = 1024,
    spatial_depth = 6,         # depth of the spatial transformer
    temporal_depth = 6,        # depth of the temporal transformer
    heads = 8,
    mlp_dim = 2048,
    variant = 'factorized_encoder', # or 'factorized_self_attention'
)

video = torch.randn(4, 3, 16, 128, 128) # (batch, channels, frames, height, width)

preds = v(video) # (4, 1000)

import torch
from vit_pytorch.parallel_vit import ViT

v = ViT(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048,
    num_parallel_branches = 2,  # in paper, they claimed 2 was optimal
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(4, 3, 256, 256)

preds = v(img) # (4, 1000)

import torch
from vit_pytorch.learnable_memory_vit import ViT, Adapter

# normal base ViT

v = ViT(
    image_size = 256,
    patch_size = 16,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(4, 3, 256, 256)
logits = v(img) # (4, 1000)

# do your usual training with ViT
# ...


# then, to finetune, just pass the ViT into the Adapter class
# you can do this for multiple Adapters, as shown below

adapter1 = Adapter(
    vit = v,
    num_classes = 2,               # number of output classes for this specific task
    num_memories_per_layer = 5     # number of learnable memories per layer, 10 was sufficient in paper
)

logits1 = adapter1(img) # (4, 2) - predict 2 classes off frozen ViT backbone with learnable memories and task specific head

# yet another task to finetune on, this time with 4 classes

adapter2 = Adapter(
    vit = v,
    num_classes = 4,
    num_memories_per_layer = 10
)

logits2 = adapter2(img) # (4, 4) - predict 4 classes off frozen ViT backbone with learnable memories and task specific head

import torch
from vit_pytorch import ViT, Dino

model = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 8,
    mlp_dim = 2048
)

learner = Dino(
    model,
    image_size = 256,
    hidden_layer = 'to_latent',        # hidden layer name or index, from which to extract the embedding
    projection_hidden_size = 256,      # projector network hidden dimension
    projection_layers = 4,             # number of layers in projection network
    num_classes_K = 65536,             # output logits dimensions (referenced as K in paper)
    student_temp = 0.9,                # student temperature
    teacher_temp = 0.04,               # teacher temperature, needs to be annealed from 0.04 to 0.07 over 30 epochs
    local_upper_crop_scale = 0.4,      # upper bound for local crop - 0.4 was recommended in the paper
    global_lower_crop_scale = 0.5,     # lower bound for global crop - 0.5 was recommended in the paper
    moving_average_decay = 0.9,        # moving average of encoder - paper showed anywhere from 0.9 to 0.999 was ok
    center_moving_average_decay = 0.9, # moving average of teacher centers - paper showed anywhere from 0.9 to 0.999 was ok
)

opt = torch.optim.Adam(learner.parameters(), lr = 3e-4)

def sample_unlabelled_images():
    return torch.randn(20, 3, 256, 256)

for _ in range(100):
    images = sample_unlabelled_images()
    loss = learner(images)
    opt.zero_grad()
    loss.backward()
    opt.step()
    learner.update_moving_average() # update moving average of teacher encoder and teacher centers

# save your improved network
torch.save(model.state_dict(), './pretrained-net.pt')

import torch
from vit_pytorch.cvt import CvT
from vit_pytorch.es_vit import EsViTTrainer

cvt = CvT(
    num_classes = 1000,
    s1_emb_dim = 64,
    s1_emb_kernel = 7,
    s1_emb_stride = 4,
    s1_proj_kernel = 3,
    s1_kv_proj_stride = 2,
    s1_heads = 1,
    s1_depth = 1,
    s1_mlp_mult = 4,
    s2_emb_dim = 192,
    s2_emb_kernel = 3,
    s2_emb_stride = 2,
    s2_proj_kernel = 3,
    s2_kv_proj_stride = 2,
    s2_heads = 3,
    s2_depth = 2,
    s2_mlp_mult = 4,
    s3_emb_dim = 384,
    s3_emb_kernel = 3,
    s3_emb_stride = 2,
    s3_proj_kernel = 3,
    s3_kv_proj_stride = 2,
    s3_heads = 4,
    s3_depth = 10,
    s3_mlp_mult = 4,
    dropout = 0.
)

learner = EsViTTrainer(
    cvt,
    image_size = 256,
    hidden_layer = 'layers',           # hidden layer name or index, from which to extract the embedding
    projection_hidden_size = 256,      # projector network hidden dimension
    projection_layers = 4,             # number of layers in projection network
    num_classes_K = 65336,             # output logits dimensions (referenced as K in paper)
    student_temp = 0.9,                # student temperature
    teacher_temp = 0.04,               # teacher temperature, needs to be annealed from 0.04 to 0.07 over 30 epochs
    local_upper_crop_scale = 0.4,      # upper bound for local crop - 0.4 was recommended in the paper
    global_lower_crop_scale = 0.5,     # lower bound for global crop - 0.5 was recommended in the paper
    moving_average_decay = 0.9,        # moving average of encoder - paper showed anywhere from 0.9 to 0.999 was ok
    center_moving_average_decay = 0.9, # moving average of teacher centers - paper showed anywhere from 0.9 to 0.999 was ok
)

opt = torch.optim.AdamW(learner.parameters(), lr = 3e-4)

def sample_unlabelled_images():
    return torch.randn(8, 3, 256, 256)

for _ in range(1000):
    images = sample_unlabelled_images()
    loss = learner(images)
    opt.zero_grad()
    loss.backward()
    opt.step()
    learner.update_moving_average() # update moving average of teacher encoder and teacher centers

# save your improved network
torch.save(cvt.state_dict(), './pretrained-net.pt')

import torch
from vit_pytorch.vit import ViT

v = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

# import Recorder and wrap the ViT

from vit_pytorch.recorder import Recorder
v = Recorder(v)

# forward pass now returns predictions and the attention maps

img = torch.randn(1, 3, 256, 256)
preds, attns = v(img)

# there is one extra patch due to the CLS token

attns # (1, 6, 16, 65, 65) - (batch x layers x heads x patch x patch)

v = v.eject()  # wrapper is discarded and original ViT instance is returned

import torch
from vit_pytorch.vit import ViT

v = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

# import Recorder and wrap the ViT

from vit_pytorch.extractor import Extractor
v = Extractor(v)

# forward pass now returns predictions and the attention maps

img = torch.randn(1, 3, 256, 256)
logits, embeddings = v(img)

# there is one extra token due to the CLS token

embeddings # (1, 65, 1024) - (batch x patches x model dim)

import torch
from vit_pytorch.cross_vit import CrossViT

v = CrossViT(
    image_size = 256,
    num_classes = 1000,
    depth = 4,
    sm_dim = 192,
    sm_patch_size = 16,
    sm_enc_depth = 2,
    sm_enc_heads = 8,
    sm_enc_mlp_dim = 2048,
    lg_dim = 384,
    lg_patch_size = 64,
    lg_enc_depth = 3,
    lg_enc_heads = 8,
    lg_enc_mlp_dim = 2048,
    cross_attn_depth = 2,
    cross_attn_heads = 8,
    dropout = 0.1,
    emb_dropout = 0.1
)

# wrap the CrossViT

from vit_pytorch.extractor import Extractor
v = Extractor(v, layer_name = 'multi_scale_encoder') # take embedding coming from the output of multi-scale-encoder

# forward pass now returns predictions and the attention maps

img = torch.randn(1, 3, 256, 256)
logits, embeddings = v(img)

# there is one extra token due to the CLS token

embeddings # ((1, 257, 192), (1, 17, 384)) - (batch x patches x dimension) <- large and small scales respectively

$ pip install nystrom-attention

import torch
from vit_pytorch.efficient import ViT
from nystrom_attention import Nystromformer

efficient_transformer = Nystromformer(
    dim = 512,
    depth = 12,
    heads = 8,
    num_landmarks = 256
)

v = ViT(
    dim = 512,
    image_size = 2048,
    patch_size = 32,
    num_classes = 1000,
    transformer = efficient_transformer
)

img = torch.randn(1, 3, 2048, 2048) # your high resolution picture
v(img) # (1, 1000)

$ pip install x-transformers

import torch
from vit_pytorch.efficient import ViT
from x_transformers import Encoder

v = ViT(
    dim = 512,
    image_size = 224,
    patch_size = 16,
    num_classes = 1000,
    transformer = Encoder(
        dim = 512,                  # set to be the same as the wrapper
        depth = 12,
        heads = 8,
        ff_glu = True,              # ex. feed forward GLU variant https://arxiv.org/abs/2002.05202
        residual_attn = True        # ex. residual attention https://arxiv.org/abs/2012.11747
    )
)

img = torch.randn(1, 3, 224, 224)
v(img) # (1, 1000)

import torch
from vit_pytorch import ViT

v = ViT(
    image_size = 256,
    patch_size = 32,
    num_classes = 1000,
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(1, 3, 256, 128) # <-- not a square

preds = v(img) # (1, 1000)

import torch
from vit_pytorch import ViT

v = ViT(
    num_classes = 1000,
    image_size = (256, 128),  # image size is a tuple of (height, width)
    patch_size = (32, 16),    # patch size is a tuple of (height, width)
    dim = 1024,
    depth = 6,
    heads = 16,
    mlp_dim = 2048,
    dropout = 0.1,
    emb_dropout = 0.1
)

img = torch.randn(1, 3, 256, 128)

preds = v(img)

@article{hassani2021escaping,
    title   = {Escaping the Big Data Paradigm with Compact Transformers},
    author  = {Ali Hassani and Steven Walton and Nikhil Shah and Abulikemu Abuduweili and Jiachen Li and Humphrey Shi},
    year    = 2021,
    url     = {https://arxiv.org/abs/2104.05704},
    eprint  = {2104.05704},
    archiveprefix = {arXiv},
    primaryclass = {cs.CV}
}

@misc{dosovitskiy2020image,
    title   = {An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
    author  = {Alexey Dosovitskiy and Lucas Beyer and Alexander Kolesnikov and Dirk Weissenborn and Xiaohua Zhai and Thomas Unterthiner and Mostafa Dehghani and Matthias Minderer and Georg Heigold and Sylvain Gelly and Jakob Uszkoreit and Neil Houlsby},
    year    = {2020},
    eprint  = {2010.11929},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{touvron2020training,
    title   = {Training data-efficient image transformers & distillation through attention},
    author  = {Hugo Touvron and Matthieu Cord and Matthijs Douze and Francisco Massa and Alexandre Sablayrolles and Hervé Jégou},
    year    = {2020},
    eprint  = {2012.12877},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{yuan2021tokenstotoken,
    title   = {Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet},
    author  = {Li Yuan and Yunpeng Chen and Tao Wang and Weihao Yu and Yujun Shi and Francis EH Tay and Jiashi Feng and Shuicheng Yan},
    year    = {2021},
    eprint  = {2101.11986},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{zhou2021deepvit,
    title   = {DeepViT: Towards Deeper Vision Transformer},
    author  = {Daquan Zhou and Bingyi Kang and Xiaojie Jin and Linjie Yang and Xiaochen Lian and Qibin Hou and Jiashi Feng},
    year    = {2021},
    eprint  = {2103.11886},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{touvron2021going,
    title   = {Going deeper with Image Transformers},
    author  = {Hugo Touvron and Matthieu Cord and Alexandre Sablayrolles and Gabriel Synnaeve and Hervé Jégou},
    year    = {2021},
    eprint  = {2103.17239},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{chen2021crossvit,
    title   = {CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification},
    author  = {Chun-Fu Chen and Quanfu Fan and Rameswar Panda},
    year    = {2021},
    eprint  = {2103.14899},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{wu2021cvt,
    title   = {CvT: Introducing Convolutions to Vision Transformers},
    author  = {Haiping Wu and Bin Xiao and Noel Codella and Mengchen Liu and Xiyang Dai and Lu Yuan and Lei Zhang},
    year    = {2021},
    eprint  = {2103.15808},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{heo2021rethinking,
    title   = {Rethinking Spatial Dimensions of Vision Transformers},
    author  = {Byeongho Heo and Sangdoo Yun and Dongyoon Han and Sanghyuk Chun and Junsuk Choe and Seong Joon Oh},
    year    = {2021},
    eprint  = {2103.16302},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{graham2021levit,
    title   = {LeViT: a Vision Transformer in ConvNet's Clothing for Faster Inference},
    author  = {Ben Graham and Alaaeldin El-Nouby and Hugo Touvron and Pierre Stock and Armand Joulin and Hervé Jégou and Matthijs Douze},
    year    = {2021},
    eprint  = {2104.01136},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{li2021localvit,
    title   = {LocalViT: Bringing Locality to Vision Transformers},
    author  = {Yawei Li and Kai Zhang and Jiezhang Cao and Radu Timofte and Luc Van Gool},
    year    = {2021},
    eprint  = {2104.05707},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{chu2021twins,
    title   = {Twins: Revisiting Spatial Attention Design in Vision Transformers},
    author  = {Xiangxiang Chu and Zhi Tian and Yuqing Wang and Bo Zhang and Haibing Ren and Xiaolin Wei and Huaxia Xia and Chunhua Shen},
    year    = {2021},
    eprint  = {2104.13840},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{su2021roformer,
    title   = {RoFormer: Enhanced Transformer with Rotary Position Embedding},
    author  = {Jianlin Su and Yu Lu and Shengfeng Pan and Bo Wen and Yunfeng Liu},
    year    = {2021},
    eprint  = {2104.09864},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}

@misc{zhang2021aggregating,
    title   = {Aggregating Nested Transformers},
    author  = {Zizhao Zhang and Han Zhang and Long Zhao and Ting Chen and Tomas Pfister},
    year    = {2021},
    eprint  = {2105.12723},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{chen2021regionvit,
    title   = {RegionViT: Regional-to-Local Attention for Vision Transformers},
    author  = {Chun-Fu Chen and Rameswar Panda and Quanfu Fan},
    year    = {2021},
    eprint  = {2106.02689},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{wang2021crossformer,
    title   = {CrossFormer: A Versatile Vision Transformer Hinging on Cross-scale Attention},
    author  = {Wenxiao Wang and Lu Yao and Long Chen and Binbin Lin and Deng Cai and Xiaofei He and Wei Liu},
    year    = {2021},
    eprint  = {2108.00154},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{caron2021emerging,
    title   = {Emerging Properties in Self-Supervised Vision Transformers},
    author  = {Mathilde Caron and Hugo Touvron and Ishan Misra and Hervé Jégou and Julien Mairal and Piotr Bojanowski and Armand Joulin},
    year    = {2021},
    eprint  = {2104.14294},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{he2021masked,
    title   = {Masked Autoencoders Are Scalable Vision Learners},
    author  = {Kaiming He and Xinlei Chen and Saining Xie and Yanghao Li and Piotr Dollár and Ross Girshick},
    year    = {2021},
    eprint  = {2111.06377},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{xie2021simmim,
    title   = {SimMIM: A Simple Framework for Masked Image Modeling},
    author  = {Zhenda Xie and Zheng Zhang and Yue Cao and Yutong Lin and Jianmin Bao and Zhuliang Yao and Qi Dai and Han Hu},
    year    = {2021},
    eprint  = {2111.09886},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{fayyaz2021ats,
    title   = {ATS: Adaptive Token Sampling For Efficient Vision Transformers},
    author  = {Mohsen Fayyaz and Soroush Abbasi Kouhpayegani and Farnoush Rezaei Jafari and Eric Sommerlade and Hamid Reza Vaezi Joze and Hamed Pirsiavash and Juergen Gall},
    year    = {2021},
    eprint  = {2111.15667},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{mehta2021mobilevit,
    title   = {MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer},
    author  = {Sachin Mehta and Mohammad Rastegari},
    year    = {2021},
    eprint  = {2110.02178},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{lee2021vision,
    title   = {Vision Transformer for Small-Size Datasets},
    author  = {Seung Hoon Lee and Seunghyun Lee and Byung Cheol Song},
    year    = {2021},
    eprint  = {2112.13492},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{renggli2022learning,
    title   = {Learning to Merge Tokens in Vision Transformers},
    author  = {Cedric Renggli and André Susano Pinto and Neil Houlsby and Basil Mustafa and Joan Puigcerver and Carlos Riquelme},
    year    = {2022},
    eprint  = {2202.12015},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@misc{yang2022scalablevit,
    title   = {ScalableViT: Rethinking the Context-oriented Generalization of Vision Transformer},
    author  = {Rui Yang and Hailong Ma and Jie Wu and Yansong Tang and Xuefeng Xiao and Min Zheng and Xiu Li},
    year    = {2022},
    eprint  = {2203.10790},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV}
}

@inproceedings{Touvron2022ThreeTE,
    title   = {Three things everyone should know about Vision Transformers},
    author  = {Hugo Touvron and Matthieu Cord and Alaaeldin El-Nouby and Jakob Verbeek and Herv'e J'egou},
    year    = {2022}
}

@inproceedings{Sandler2022FinetuningIT,
    title   = {Fine-tuning Image Transformers using Learnable Memory},
    author  = {Mark Sandler and Andrey Zhmoginov and Max Vladymyrov and Andrew Jackson},
    year    = {2022}
}

@inproceedings{Li2022SepViTSV,
    title   = {SepViT: Separable Vision Transformer},
    author  = {Wei Li and Xing Wang and Xin Xia and Jie Wu and Xuefeng Xiao and Minghang Zheng and Shiping Wen},
    year    = {2022}
}

@inproceedings{Tu2022MaxViTMV,
    title   = {MaxViT: Multi-Axis Vision Transformer},
    author  = {Zhengzhong Tu and Hossein Talebi and Han Zhang and Feng Yang and Peyman Milanfar and Alan Conrad Bovik and Yinxiao Li},
    year    = {2022}
}

@article{Li2021EfficientSV,
    title   = {Efficient Self-supervised Vision Transformers for Representation Learning},
    author  = {Chunyuan Li and Jianwei Yang and Pengchuan Zhang and Mei Gao and Bin Xiao and Xiyang Dai and Lu Yuan and Jianfeng Gao},
    journal = {ArXiv},
    year    = {2021},
    volume  = {abs/2106.09785}
}

@misc{Beyer2022BetterPlainViT
    title     = {Better plain ViT baselines for ImageNet-1k},
    author    = {Beyer, Lucas and Zhai, Xiaohua and Kolesnikov, Alexander},
    publisher = {arXiv},
    year      = {2022}
}

@article{Arnab2021ViViTAV,
    title   = {ViViT: A Video Vision Transformer},
    author  = {Anurag Arnab and Mostafa Dehghani and Georg Heigold and Chen Sun and Mario Lucic and Cordelia Schmid},
    journal = {2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
    year    = {2021},
    pages   = {6816-6826}
}

@article{Liu2022PatchDropoutEV,
    title   = {PatchDropout: Economizing Vision Transformers Using Patch Dropout},
    author  = {Yue Liu and Christos Matsoukas and Fredrik Strand and Hossein Azizpour and Kevin Smith},
    journal = {ArXiv},
    year    = {2022},
    volume  = {abs/2208.07220}
}

@misc{https://doi.org/10.48550/arxiv.2302.01327,
    doi     = {10.48550/ARXIV.2302.01327},
    url     = {https://arxiv.org/abs/2302.01327},
    author  = {Kumar, Manoj and Dehghani, Mostafa and Houlsby, Neil},
    title   = {Dual PatchNorm},
    publisher = {arXiv},
    year    = {2023},
    copyright = {Creative Commons Attribution 4.0 International}
}

@inproceedings{Dehghani2023PatchNP,
    title   = {Patch n' Pack: NaViT, a Vision Transformer for any Aspect Ratio and Resolution},
    author  = {Mostafa Dehghani and Basil Mustafa and Josip Djolonga and Jonathan Heek and Matthias Minderer and Mathilde Caron and Andreas Steiner and Joan Puigcerver and Robert Geirhos and Ibrahim M. Alabdulmohsin and Avital Oliver and Piotr Padlewski and Alexey A. Gritsenko and Mario Luvci'c and Neil Houlsby},
    year    = {2023}
}

@misc{vaswani2017attention,
    title   = {Attention Is All You Need},
    author  = {Ashish Vaswani and Noam Shazeer and Niki Parmar and Jakob Uszkoreit and Llion Jones and Aidan N. Gomez and Lukasz Kaiser and Illia Polosukhin},
    year    = {2017},
    eprint  = {1706.03762},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL}
}

@inproceedings{dao2022flashattention,
    title   = {Flash{A}ttention: Fast and Memory-Efficient Exact Attention with {IO}-Awareness},
    author  = {Dao, Tri and Fu, Daniel Y. and Ermon, Stefano and Rudra, Atri and R{\'e}, Christopher},
    booktitle = {Advances in Neural Information Processing Systems},
    year    = {2022}
}

@inproceedings{Darcet2023VisionTN,
    title   = {Vision Transformers Need Registers},
    author  = {Timoth'ee Darcet and Maxime Oquab and Julien Mairal and Piotr Bojanowski},
    year    = {2023},
    url     = {https://api.semanticscholar.org/CorpusID:263134283}
}

@inproceedings{ElNouby2021XCiTCI,
    title   = {XCiT: Cross-Covariance Image Transformers},
    author  = {Alaaeldin El-Nouby and Hugo Touvron and Mathilde Caron and Piotr Bojanowski and Matthijs Douze and Armand Joulin and Ivan Laptev and Natalia Neverova and Gabriel Synnaeve and Jakob Verbeek and Herv{\'e} J{\'e}gou},
    booktitle = {Neural Information Processing Systems},
    year    = {2021},
    url     = {https://api.semanticscholar.org/CorpusID:235458262}
}

@inproceedings{Koner2024LookupViTCV,
    title   = {LookupViT: Compressing visual information to a limited number of tokens},
    author  = {Rajat Koner and Gagan Jain and Prateek Jain and Volker Tresp and Sujoy Paul},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:271244592}
}

@article{Bao2022AllAW,
    title   = {All are Worth Words: A ViT Backbone for Diffusion Models},
    author  = {Fan Bao and Shen Nie and Kaiwen Xue and Yue Cao and Chongxuan Li and Hang Su and Jun Zhu},
    journal = {2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    year    = {2022},
    pages   = {22669-22679},
    url     = {https://api.semanticscholar.org/CorpusID:253581703}
}

@misc{Rubin2024,
    author  = {Ohad Rubin},
    url     = {https://medium.com/@ohadrubin/exploring-weight-decay-in-layer-normalization-challenges-and-a-reparameterization-solution-ad4d12c24950}
}

@inproceedings{Loshchilov2024nGPTNT,
    title   = {nGPT: Normalized Transformer with Representation Learning on the Hypersphere},
    author  = {Ilya Loshchilov and Cheng-Ping Hsieh and Simeng Sun and Boris Ginsburg},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273026160}
}

@inproceedings{Liu2017DeepHL,
    title   = {Deep Hyperspherical Learning},
    author  = {Weiyang Liu and Yanming Zhang and Xingguo Li and Zhen Liu and Bo Dai and Tuo Zhao and Le Song},
    booktitle = {Neural Information Processing Systems},
    year    = {2017},
    url     = {https://api.semanticscholar.org/CorpusID:5104558}
}

@inproceedings{Zhou2024ValueRL,
    title   = {Value Residual Learning For Alleviating Attention Concentration In Transformers},
    author  = {Zhanchao Zhou and Tianyi Wu and Zhiyun Jiang and Zhenzhong Lan},
    year    = {2024},
    url     = {https://api.semanticscholar.org/CorpusID:273532030}
}

@article{Zhu2024HyperConnections,
    title   = {Hyper-Connections},
    author  = {Defa Zhu and Hongzhi Huang and Zihao Huang and Yutao Zeng and Yunyao Mao and Banggu Wu and Qiyang Min and Xun Zhou},
    journal = {ArXiv},
    year    = {2024},
    volume  = {abs/2409.19606},
    url     = {https://api.semanticscholar.org/CorpusID:272987528}
}

@inproceedings{Fuller2025SimplerFV,
    title   = {Simpler Fast Vision Transformers with a Jumbo CLS Token},
    author  = {Anthony Fuller and Yousef Yassin and Daniel G. Kyrollos and Evan Shelhamer and James R. Green},
    year    = {2025},
    url     = {https://api.semanticscholar.org/CorpusID:276557720}
}

@misc{xiong2025ndrope,
    author = {Jerry Xiong},
    title  = {On n-dimensional rotary positional embeddings},
    year   = {2025},
    url    = {https://jerryxio.ng/posts/nd-rope/}
}

@inproceedings{anonymous2025vat,
    title   = {{VAT}: Vision Action Transformer by Unlocking Full Representation of ViT},
    author  = {Anonymous},
    booktitle = {Submitted to The Fourteenth International Conference on Learning Representations},
    year    = {2025},
    url     = {https://openreview.net/forum?id=TalHOvvLZu},
    note    = {under review}
}

@misc{carrigg2025decorrelationspeedsvisiontransformers,
    title   = {Decorrelation Speeds Up Vision Transformers},
    author  = {Kieran Carrigg and Rob van Gastel and Melda Yeghaian and Sander Dalm and Faysal Boughorbel and Marcel van Gerven},
    year    = {2025},
    eprint  = {2510.14657},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV},
    url     = {https://arxiv.org/abs/2510.14657},
}

@misc{gopalakrishnan2025decouplingwhatwherepolar,
    title   = {Decoupling the "What" and "Where" With Polar Coordinate Positional Embeddings},
    author  = {Anand Gopalakrishnan and Robert Csordás and Jürgen Schmidhuber and Michael C. Mozer},
    year    = {2025},
    eprint  = {2509.10534},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2509.10534},
}

@misc{qiu2025gatedattentionlargelanguage,
    title   = {Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free},
    author  = {Zihan Qiu and Zekun Wang and Bo Zheng and Zeyu Huang and Kaiyue Wen and Songlin Yang and Rui Men and Le Yu and Fei Huang and Suozhi Huang and Dayiheng Liu and Jingren Zhou and Junyang Lin},
    year    = {2025},
    eprint  = {2505.06708},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL},
    url     = {https://arxiv.org/abs/2505.06708}
}

@misc{chen2026postlayernormbackstableexpressive,
    title   = {Post-LayerNorm Is Back: Stable, ExpressivE, and Deep},
    author  = {Chen Chen and Lai Wei},
    year    = {2026},
    eprint  = {2601.19895},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2601.19895},
}

@misc{intelligence2025pi06vlalearnsexperience,
      title     = {$\pi^{*}_{0.6}$: a VLA That Learns From Experience},
      author    = {Physical Intelligence and Ali Amin and Raichelle Aniceto and Ashwin Balakrishna and Kevin Black and Ken Conley and Grace Connors and James Darpinian and Karan Dhabalia and Jared DiCarlo and Danny Driess and Michael Equi and Adnan Esmail and Yunhao Fang and Chelsea Finn and Catherine Glossop and Thomas Godden and Ivan Goryachev and Lachy Groom and Hunter Hancock and Karol Hausman and Gashon Hussein and Brian Ichter and Szymon Jakubczak and Rowan Jen and Tim Jones and Ben Katz and Liyiming Ke and Chandra Kuchi and Marinda Lamb and Devin LeBlanc and Sergey Levine and Adrian Li-Bell and Yao Lu and Vishnu Mano and Mohith Mothukuri and Suraj Nair and Karl Pertsch and Allen Z. Ren and Charvi Sharma and Lucy Xiaoyang Shi and Laura Smith and Jost Tobias Springenberg and Kyle Stachowicz and Will Stoeckle and Alex Swerdlow and James Tanner and Marcel Torne and Quan Vuong and Anna Walling and Haohuan Wang and Blake Williams and Sukwon Yoo and Lili Yu and Ury Zhilinsky and Zhiyuan Zhou},
      year      = {2025},
      eprint    = {2511.14759},
      archivePrefix = {arXiv},
      primaryClass = {cs.LG},
      url       = {https://arxiv.org/abs/2511.14759},
}

@misc{kimiteam2026attentionresiduals,
    title   = {Attention Residuals},
    author  = {Kimi Team and Guangyu Chen and Yu Zhang and Jianlin Su and Weixin Xu and Siyuan Pan and Yaoyu Wang and Yucheng Wang and Guanduo Chen and Bohong Yin and Yutian Chen and Junjie Yan and Ming Wei and Y. Zhang and Fanqing Meng and Chao Hong and Xiaotong Xie and Shaowei Liu and Enzhe Lu and Yunpeng Tai and Yanru Chen and Xin Men and Haiqing Guo and Y. Charles and Haoyu Lu and Lin Sui and Jinguo Zhu and Zaida Zhou and Weiran He and Weixiao Huang and Xinran Xu and Yuzhi Wang and Guokun Lai and Yulun Du and Yuxin Wu and Zhilin Yang and Xinyu Zhou},
    year    = {2026},
    eprint  = {2603.15031},
    archivePrefix = {arXiv},
    primaryClass = {cs.CL},
    url     = {https://arxiv.org/abs/2603.15031},
}

@misc{balestriero2025lejepa,
    title   = {LeJEPA: Provable and Scalable Self-Supervised Learning Without the Heuristics},
    author  = {Randall Balestriero and Yann LeCun},
    year    = {2025},
    eprint  = {2511.08544},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    url     = {https://arxiv.org/abs/2511.08544},
}

@misc{oh2026revisitingresidualconnectionsorthogonal,
    title   = {Revisiting Residual Connections: Orthogonal Updates for Stable and Efficient Deep Networks}, 
    author  = {Giyeong Oh and Woohyun Cho and Siyeol Kim and Suhwan Choi and Youngjae Yu},
    year    = {2026},
    eprint  = {2505.11881},
    archivePrefix = {arXiv},
    primaryClass = {cs.CV},
    url     = {https://arxiv.org/abs/2505.11881}, 
}

pip install vit-pytorch