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Richard Abishai
Richard Abishai

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CNN vs Transformer – A Visual Comparison

#deeplearning #vision #ai #machinelearning

How machines learn to see — locally vs globally.

If you’ve ever wondered why Vision Transformers (ViTs) replaced Convolutional Neural Networks (CNNs) so quickly in computer vision, you’re not alone.

Both models “see” — but they see differently.

Let’s visualize how these architectures process the same image step-by-step, and why attention has changed the way machines perceive the world.

🧩 1. How CNNs See: The Local Lens

A CNN processes an image piece by piece — a mosaic of local patterns.

  • Each convolution filter slides over pixels (a receptive field)
  • Early layers learn edges, textures, shapes
  • Deeper layers combine them into higher-level features (eyes, wheels, leaves) 
import torch
import torch.nn as nn

cnn = nn.Sequential(
    nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(2),
    nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1),
    nn.ReLU()
)

print(sum(p.numel() for p in cnn.parameters()), "trainable parameters")
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Visual metaphor:
→ CNNs are like looking through a microscope — powerful, but only one patch at a time.
Local precision, global blindness.

🌍 2. How Transformers See: The Global Canvas

Transformers treat an image as a sequence of patches, not pixels.
Each patch becomes a token, similar to a word in NLP.

Instead of convolutions, a self-attention layer learns which patches matter to each other —
so the model can connect “eye” to “face,” or “wheel” to “car,” even if they’re far apart.

from transformers import ViTModel, ViTFeatureExtractor
import torch
from PIL import Image
import requests

url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/image_classification.jpeg"
image = Image.open(requests.get(url, stream=True).raw)

extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224")
model = ViTModel.from_pretrained("google/vit-base-patch16-224")

inputs = extractor(images=image, return_tensors="pt")
outputs = model(**inputs)
print("Hidden state shape:", outputs.last_hidden_state.shape)
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Visual metaphor:
→ ViTs are like seeing from above — every part of the image talks to every other part.
Global awareness, context-rich understanding.

🔬 3. Visualizing the Difference

Let’s see this difference side-by-side:

Concept CNN Transformer
Vision style Local → Hierarchical Global → Relational
Input type Pixels Patches
Core operation Convolution Self-Attention
Memory Spatial (fixed window) Contextual (dynamic)
Inductive bias Strong (translation invariant) Minimal (learns from data)
Pretraining need Works from scratch Needs large datasets
Best for Small data, simple patterns Complex, global reasoning

🧠 4. Why Transformers Surpass CNNs (Eventually)

Transformers outperform CNNs when:

You have lots of data

You need long-range dependencies

You want to unify vision and language

But CNNs are still valuable — fast, efficient, and great at edge devices.
The real magic is in hybrid architectures — CNN + Attention (ConvNeXt, CoAtNet, etc.)
They combine the sharpness of convolution with the context of attention.

🧪 5. Minimal Code Comparison

Here’s a quick benchmark-style code snippet using PyTorch:

import torchvision.models as models
import torch

cnn_model = models.resnet18(pretrained=True)
vit_model = models.vit_b_16(pretrained=True)

x = torch.randn(1, 3, 224, 224)
cnn_out = cnn_model(x)
vit_out = vit_model(x)

print("CNN output:", cnn_out.shape)
print("ViT output:", vit_out.shape)
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Output:

CNN output: torch.Size([1, 1000])
ViT output: torch.Size([1, 1000])
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Same input, same output shape — completely different thought process.

🪞 6. The Philosophy Behind It

CNNs extract meaning.
Transformers connect meaning.

One builds understanding layer by layer.
The other builds it all at once — like a conversation, not a hierarchy.

Deep learning started with perception.
Transformers added awareness.

That’s the real leap.

⚡ 7. The Takeaway

CNNs = strong inductive bias, fast training, efficient on small data

Transformers = flexible reasoning, global context, scalability

Hybrids = the best of both worlds

Both architectures are tools — what matters is when to use which.

Use CNNs when your world is small.
Use Transformers when your world is connected.

Next Up → “Fine-Tuning Failures and Fixes” — my notes from debugging unstable Transformer training runs.

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