Tech Matchups: Vision Transformers vs Convolutional Networks
Overview
Vision Transformers (ViT) apply the transformer architecture to image patches, achieving state-of-the-art results by modeling global relationships from the first layer, while Convolutional Neural Networks (CNNs) leverage local receptive fields and hierarchical feature extraction through learned filters.
ViTs require large datasets (>14M images) to outperform CNNs but provide better scalability to high-resolution images and stronger out-of-distribution generalization.
Breakthrough: ViT-H/14 achieved 88.55% ImageNet top-1 accuracy (JFT-300M pretraining), surpassing EfficientNet's 88.36%.
Section 1 - Architectural Comparison
CNN Feature Extraction:
# ResNet-50 Architecture
def residual_block(x, filters, stride=1):
shortcut = x
x = Conv2D(filters, (3,3), strides=stride, padding='same')(x)
x = BatchNormalization()(x)
x = ReLU()(x)
x = Conv2D(filters, (3,3), padding='same')(x)
x = BatchNormalization()(x)
if stride != 1:
shortcut = Conv2D(filters, (1,1), strides=stride)(shortcut)
x = Add()([x, shortcut])
return ReLU()(x)
ViT Patch Processing:
# Vision Transformer Pseudocode
class ViT(nn.Module):
def __init__(self, image_size=224, patch_size=16, dim=768):
num_patches = (image_size // patch_size) ** 2
self.patch_embed = nn.Conv2d(3, dim, patch_size, patch_size)
self.pos_embed = nn.Parameter(torch.randn(1, num_patches+1, dim))
self.cls_token = nn.Parameter(torch.randn(1, 1, dim))
self.transformer = TransformerEncoder(dim, num_heads=12)
def forward(self, x):
# [B, C, H, W] -> [B, num_patches, dim]
x = self.patch_embed(x).flatten(2).transpose(1,2)
x = torch.cat([self.cls_token.expand(x.shape[0], -1, -1), x], dim=1)
x += self.pos_embed
return self.transformer(x)
- Receptive Fields: CNNs build local-to-global understanding, ViTs have global attention from layer 1
- Translation Equivariance: CNNs have built-in via convolutions, ViTs must learn it
- Scaling Behavior: ViTs show log-linear scaling beyond 100M params, CNNs plateau earlier
Section 2 - Performance Comparison
ImageNet Benchmark (Top-1 Accuracy):
| Model | Params | Accuracy | Throughput (imgs/sec) |
|---|---|---|---|
| ViT-L/16 | 304M | 85.3% | 892 |
| EfficientNet-B7 | 66M | 84.7% | 1,024 |
| ConvNeXt-XL | 350M | 87.8% | 756 |
Data Efficiency:
- JFT-300M: ViTs outperform CNNs by 2-4% accuracy
- ImageNet-1K: CNNs outperform ViTs by 1-3% without pretraining
Section 3 - Hybrid Architectures
Convolutional Stem Variants:
# Hybrid Architecture Example
class HybridViT(nn.Module):
def __init__(self):
self.conv_stem = nn.Sequential(
nn.Conv2d(3, 64, 7, stride=2, padding=3),
nn.MaxPool2d(3, stride=2, padding=1),
ResNetBlock(64, 128)
)
self.vit = ViT(input_dim=128, ...)
Recent Advances:
- MobileViT: Combines mobile-friendly convolutions with transformer blocks
- ConvNeXt: Modernized CNN architecture adopting ViT design principles