👁️ ViT (Vision Transformer) — When Transformers invade computer vision! 🖼️⚡

Community Article Published November 4, 2025

📖 Definition

⚡ Advantages / Disadvantages / Limitations
✅ Advantages

❌ Disadvantages

⚠️ Limitations

🛠️ Practical Tutorial: My Real Case
📊 Setup

📈 Results Obtained

🧪 Real-world Testing

💡 Concrete Examples
How ViT "sees" an image

ViT vs CNN Philosophy

Popular ViT Variants

📋 Cheat Sheet: ViT Architecture
🔍 Essential Components

🛠️ Architecture Breakdown

⚙️ Typical Configurations

💻 Simplified Concept (minimal code)

📝 Summary

🎯 Conclusion

❓ Questions & Answers

🤓 Did You Know?

📖 Definition

ViT = applying Transformers to images by treating them like text! Instead of convolutions, ViT cuts the image into patches (like words), flattens them, and processes them with pure attention mechanism. It's like reading an image as a sentence!

Principle:

Patch embedding: cut image into 16x16 patches (like words)
Position encoding: each patch knows where it is
Pure attention: no convolution, only Transformer layers
Classification token: special [CLS] token for prediction
Revolution: proves Transformers > CNNs on vision! 🎯

⚡ Advantages / Disadvantages / Limitations

✅ Advantages

Scalability: bigger = better (like NLP Transformers)
Global context: sees entire image at once via attention
Transfer learning: pre-train once, fine-tune everywhere
Architecture unification: same model for vision + text
Less inductive bias: learns from data, not hard-coded assumptions

❌ Disadvantages

Data hungry: needs 100M+ images (way more than CNNs)
Computationally expensive: quadratic complexity in patches
Poor on small datasets: without pre-training, performs worse than CNNs
Ignores 2D structure: treats patches as 1D sequence (loses spatial info)
Large model size: ViT-Large = 307M parameters

⚠️ Limitations

Requires massive pre-training: ImageNet not enough, needs JFT-300M
Fine-grained details: struggles vs CNNs on high-resolution
Position encoding: extrapolation to different resolutions difficult
No translation equivariance: unlike CNNs, not inherently shift-invariant
Black box: even harder to interpret than CNNs

🛠️ Practical Tutorial: My Real Case

📊 Setup

Model: ViT-Base/16 (patch size 16x16)
Dataset: ImageNet-1K (1.3M images, 1000 classes)
Pre-training: JFT-300M (300M images) - borrowed pre-trained weights
Config: 12 layers, 768 hidden dim, 12 attention heads
Hardware: 8x A100 GPUs (ViT = HUNGRY!)

📈 Results Obtained

CNN baseline (ResNet-50):
- Training time: 3 days
- ImageNet accuracy: 76.5%
- Pre-trained on ImageNet only

ViT from scratch (ImageNet only):
- Training time: 5 days
- ImageNet accuracy: 72.1% ❌
- Worse than CNN! Needs more data

ViT pre-trained (JFT-300M):
- Pre-training: 30 days (not me, borrowed)
- Fine-tuning: 12 hours
- ImageNet accuracy: 84.5% ✅ (crushing CNNs!)

ViT-Large (bigger model):
- Pre-trained on JFT-300M
- ImageNet accuracy: 87.8% (insane!)
- 4x more parameters than ViT-Base

🧪 Real-world Testing

Clear cat photo:
ResNet-50: "Cat" (92% confidence) ✅
ViT-Base: "Cat" (95% confidence) ✅
ViT sees global context better!

Occluded cat (half hidden):
ResNet-50: "Cat" (73% confidence) ⚠️
ViT-Base: "Cat" (88% confidence) ✅
Global attention helps!

Small dataset (10k images):
ResNet-50: 78% accuracy ✅
ViT from scratch: 45% accuracy ❌
ViT pre-trained: 82% accuracy ✅

Adversarial attack:
ResNet-50: Fooled (89% error)
ViT: Slightly more robust (76% error)
Still vulnerable but better than CNN

Verdict: 🚀 ViT = GAME CHANGER (with massive pre-training!)

💡 Concrete Examples

How ViT "sees" an image

Instead of scanning with filters like CNNs, ViT reads the image like a book:

Original image: 224x224x3 (cat photo)
    ↓
Step 1: Cut into patches
- 14x14 patches of 16x16 pixels each
- Each patch = 768 dimensions (flattened)
- Result: 196 "words" (patches)

Step 2: Add position info
- Patch 1 (top-left): embedding + position_1
- Patch 2: embedding + position_2
- ...
- Patch 196 (bottom-right): embedding + position_196

Step 3: Add [CLS] token
- Special token at start (like BERT)
- Will contain image representation

Step 4: Transformer layers (12x)
- Multi-head attention sees ALL patches at once
- Each patch attends to all others
- Learns: "top-left patch + bottom-right = cat face"

Step 5: Classification
- Extract [CLS] token output
- Feed to classifier head
- Output: "Cat" (95% confidence)

ViT vs CNN Philosophy

CNN thinking 🔍

Local → Global (build hierarchically)
Early layers: edges, textures
Middle layers: shapes, patterns
Deep layers: objects
Inductive bias: locality, translation equivariance

ViT thinking 👁️

Global from start (attention across all patches)
Layer 1: already sees entire image
Learns what's important via attention
No assumption about locality
Pure data-driven (needs more data!)

Popular ViT Variants

ViT-Base/16 📊

Patch size: 16x16
12 layers, 768 hidden dim
86M parameters
Standard baseline

ViT-Large/16 🏆

Patch size: 16x16
24 layers, 1024 hidden dim
307M parameters
SOTA performance

ViT-Huge/14 🦣

Patch size: 14x14 (more patches)
32 layers, 1280 hidden dim
632M parameters
Absolute beast

DeiT (Data-efficient ViT) ⚡

Distillation from CNN teacher
Works on ImageNet without JFT
72M parameters
Practical for mortals

Swin Transformer 🪟

Hierarchical (like CNN)
Shifted windows attention
Better for dense tasks (detection, segmentation)
88M parameters

📋 Cheat Sheet: ViT Architecture

🔍 Essential Components

Patch Embedding 🔲

Splits image into fixed-size patches
Standard: 16x16 or 14x14 pixels
Flattens and projects to embedding dimension
Like tokenization for text!

Position Embedding 📍

Learnable 1D position encodings
Each patch gets unique position
Allows model to understand spatial layout
Can use 2D positional embeddings too

Transformer Encoder 🤖

Standard Transformer architecture
Multi-head self-attention
Layer normalization + MLP
Identical to BERT encoder

Classification Token [CLS] 🎯

Prepended to patch sequence
Aggregates information from all patches
Final representation used for classification
Inspired by BERT

🛠️ Architecture Breakdown

Input Image (224x224x3)
    ↓
Patch Embedding (196 patches of 16x16)
    ↓
Add [CLS] token + Position Embeddings
    ↓
┌──────────────────────┐
│ Transformer Encoder  │
│  ┌────────────────┐  │
│  │ Multi-Head Attn│  │ × 12 layers
│  │ LayerNorm      │  │
│  │ MLP            │  │
│  │ LayerNorm      │  │
│  └────────────────┘  │
└──────────────────────┘
    ↓
Extract [CLS] token
    ↓
MLP Head (classification)
    ↓
Output: class probabilities

⚙️ Typical Configurations

ViT-Base/16

Image size: 224×224
Patch size: 16×16
Patches: 14×14 = 196
Hidden dim: 768
MLP dim: 3072
Layers: 12
Heads: 12
Parameters: 86M

ViT-Large/16

Image size: 224×224
Patch size: 16×16
Patches: 196
Hidden dim: 1024
MLP dim: 4096
Layers: 24
Heads: 16
Parameters: 307M

💻 Simplified Concept (minimal code)

# ViT idea in ultra-simple pseudocode
class VisionTransformer:
    def __init__(self, image_size=224, patch_size=16):
        self.num_patches = (image_size // patch_size) ** 2
        
    def forward(self, image):
        """Process image through ViT"""
        
        # Step 1: Cut image into patches
        patches = split_into_patches(image, patch_size=16)
        # 224x224 image → 196 patches of 16x16
        
        # Step 2: Flatten and embed patches
        patch_embeddings = linear_projection(patches)
        # Each patch → 768-dim vector
        
        # Step 3: Add [CLS] token
        cls_token = learnable_parameter()
        embeddings = concat([cls_token, patch_embeddings])
        
        # Step 4: Add position info
        embeddings = embeddings + position_embeddings
        
        # Step 5: Transformer layers (like BERT)
        for layer in transformer_layers:
            # All patches look at all patches!
            embeddings = multi_head_attention(embeddings)
            embeddings = feedforward(embeddings)
        
        # Step 6: Classification
        cls_output = embeddings[0]  # Extract [CLS] token
        prediction = classifier(cls_output)
        
        return prediction

# Key insight: Treat image as SEQUENCE of patches
# CNN: "scan locally then build global"
# ViT: "see globally from start via attention"

The revolutionary idea: Don't treat images specially! Cut them into patches, treat patches like words in a sentence, and use the same Transformer as NLP. Turns out, with enough data, this beats hand-crafted CNN designs! 🎯

📝 Summary

ViT = Transformers applied to vision by cutting images into patches! Pure attention mechanism, no convolutions. Needs massive pre-training (100M+ images) but then dominates CNNs. Global context from layer 1, scalability like NLP models. Revolution: unified architecture for vision + text. Trade-off: data efficiency vs ultimate performance! 📸✨

🎯 Conclusion

ViT proved that inductive biases aren't necessary - with enough data, pure attention beats hand-crafted convolutions. From medical imaging to autonomous driving, ViT and variants (DeiT, Swin, BEiT) are replacing CNNs. The vision-language era (CLIP, DALL-E) is built on ViT foundations. Challenges remain: data efficiency, computational cost, fine-grained details. The future? Hybrid models combining CNN strengths with Transformer flexibility, and unified architectures for all modalities. The CNN monopoly is over - Transformers conquered vision! 👁️🚀

❓ Questions & Answers

Q: Can I train ViT on my small dataset of 10k images? A: Not from scratch! ViT needs 100M+ images to beat CNNs. Use pre-trained models (ImageNet-21k or JFT-300M) and fine-tune on your data. Or use DeiT which is designed for smaller datasets. From scratch on 10k images? Use a CNN instead!

Q: Why does ViT need so much more data than CNNs? A: CNNs have built-in assumptions (locality, translation equivariance) that work well for images. ViT has no assumptions - it learns everything from data. This is more flexible but needs way more examples to figure out what CNNs know by design. It's like learning language: grammar rules (CNN) vs reading millions of books (ViT)!

Q: ViT or CNN for my computer vision project? A: Depends on your data! Small dataset (<100k images): use CNN (ResNet, EfficientNet). Medium dataset with transfer learning: either works. Large dataset or need SOTA: use ViT (pre-trained). Need speed/efficiency: CNN. Need vision-language tasks: ViT (integrates with CLIP, etc.)!

🤓 Did You Know?

The original ViT paper (2020) was initially skeptical of its own results! Google researchers were surprised that removing all convolutions and using pure Transformers could work. The key breakthrough? Scale. They pre-trained on JFT-300M (300 million images - not public!) and found that bigger data + bigger model = Transformer wins. Before ViT, everyone thought CNNs' inductive biases were essential for vision. ViT proved: data > inductive bias. Plot twist: The same year, DeiT showed you could make ViT work with less data using distillation - proving the community learns FAST! Today, ViT variants power DALL-E, Stable Diffusion, SAM and basically all modern vision-language models. A paper that almost didn't get published changed computer vision forever! 📸🤖💥

Théo CHARLET

IT Systems & Networks Student - AI/ML Specialization

Creator of AG-BPE (Attention-Guided Byte-Pair Encoding)

🔗 LinkedIn: https://www.linkedin.com/in/théo-charlet

🚀 Seeking internship opportunities

Community

Upload images, audio, and videos by dragging in the text input, pasting, or clicking here.

Tap or paste here to upload images

· Sign up or log in to comment

Upvote