ViT Large

ViT (Vision Transformer) Large is the larger variant of the Vision Transformer architecture, featuring 304 million parameters. It processes images by splitting them into patches and applying transformer layers with self-attention mechanisms. ViT Large delivers state-of-the-art accuracy on image classification benchmarks when sufficient training data is available.

Video Tutorials

Learn how to train and deploy ViT Large models:

• Train a Computer Vision Model - Complete walkthrough using ViT Large for image classification
• Run Inference on Computer Vision Models - How to use trained models for predictions
• Deploy Computer Vision Models - Production deployment strategies

When to Use ViT Large

ViT Large is optimal for scenarios requiring:

• Maximum accuracy where performance is the top priority
• Large datasets (10,000+ images) that can leverage the model's capacity
• High-quality training infrastructure with powerful GPUs (16GB+ VRAM)
• Applications where inference latency is acceptable in exchange for accuracy gains

Choose ViT Large for production systems, research applications, or competitions where achieving the highest possible accuracy justifies the computational cost.

Strengths

• Highest accuracy: Best-in-class performance on image classification benchmarks
• Rich representations: Large capacity captures subtle visual features and patterns
• Global attention: Processes entire image context from first layer
• Strong transfer learning: Pre-trained weights transfer exceptionally well to new domains
• Scalable architecture: Proven to scale effectively with more data and compute

Weaknesses

• Very data hungry: Requires substantial training data to avoid overfitting (10k+ images recommended)
• High computational cost: 4x more parameters than ViT Base, significantly slower training
• Large memory footprint: Requires 16-24GB GPU VRAM for training
• Slow inference: 3-4x slower than ResNet models at inference time
• Overkill for simple tasks: Unnecessarily complex for straightforward classification problems

Architecture Overview

Large Transformer Design

ViT Large uses a deeper and wider transformer architecture:

1. Patch Embedding: 224x224 images split into 16x16 patches (196 patches)
2. High-dimensional Projection: Each patch projected to 1024 dimensions
3. Position Embeddings: Learnable positional encodings added
4. Deep Transformer: 24 layers with 16 attention heads per layer
5. Classification Head: MLP projecting [CLS] token to class logits

Key Specifications:

• Hidden size: 1024
• Number of layers: 24
• Attention heads: 16
• Patch size: 16x16
• Parameters: ~304M

Parameters

Training Configuration

Training Images

• Type: Folder
• Description: Directory containing training images organized in class subfolders
• Format: Subfolder names are class labels
• Required: Yes
• Minimum recommended: 10,000+ images for optimal results

Batch Size (Default: 4)

• Range: 2-16 (heavily dependent on GPU memory)
• Recommendation:
  • 2-4 for 16GB GPU
  • 8-16 for 24GB+ GPU
  • Start with 4 and reduce if OOM errors occur
• Impact: Larger batches provide more stable gradients but require significantly more memory

Epochs (Default: 1)

• Range: 1-15
• Recommendation:
  • 1-2 epochs for very large datasets (>50k images)
  • 3-5 epochs for large datasets (10k-50k images)
  • 5-10 epochs for medium datasets (5k-10k images)
  • Not recommended for small datasets (<5k images)
• Impact: More epochs needed to converge due to model size, but risk of overfitting increases

Learning Rate (Default: 5e-5)

• Range: 1e-6 to 1e-4
• Recommendation:
  • 5e-5 for standard fine-tuning with balanced data
  • 2e-5 for small to medium datasets
  • 1e-4 for very large datasets with many classes
• Impact: ViT Large is sensitive to learning rate; too high causes instability

Eval Steps (Default: 1)

• Description: Evaluation frequency during training (1 = after each epoch)
• Recommendation: Keep at 1 to monitor training progress closely
• Impact: Frequent evaluation helps catch overfitting early

Configuration Tips

Dataset Size Recommendations

Small Datasets (<5,000 images)

• Not recommended - Use ResNet-50 or ViT Base instead
• ViT Large will likely overfit severely
• If you must use: learning_rate=1e-5, epochs=3-5, extensive augmentation

Medium Datasets (5,000-10,000 images)

• Marginal choice - consider ViT Base or ResNet-101
• Configuration: learning_rate=2e-5, epochs=5-8, batch_size=4
• Heavy data augmentation essential
• Monitor validation metrics very closely for overfitting

Large Datasets (10,000-50,000 images)

• Good choice - ViT Large starts to show advantages
• Configuration: learning_rate=5e-5, epochs=3-5, batch_size=8-16
• Standard augmentation sufficient
• Expect 2-5% accuracy improvement over ViT Base

Very Large Datasets (>50,000 images)

• Excellent choice - optimal use of ViT Large's capacity
• Configuration: learning_rate=5e-5 to 1e-4, epochs=1-3, batch_size=16
• Light augmentation, focus on data quality
• Maximum accuracy gains compared to smaller models

Fine-tuning Best Practices

1. Start with Short Training: Begin with 1-2 epochs to gauge convergence speed
2. Monitor Memory: Watch GPU memory usage; ViT Large can hit limits quickly
3. Use Mixed Precision: Enable FP16/BF16 training to reduce memory and increase speed
4. Validate Frequently: Check validation metrics after each epoch due to overfitting risk
5. Learning Rate Warmup: Consider gradual learning rate increase for first 10% of training
6. Gradient Clipping: May help stabilize training with aggressive learning rates

Hardware Requirements

Minimum Configuration

• GPU: 16GB VRAM (NVIDIA RTX 3080Ti/4090 or A4000)
• RAM: 32GB system memory
• Storage: 1GB for model weights + dataset size

Recommended Configuration

• GPU: 24GB VRAM (NVIDIA RTX 4090 or A5000/A6000)
• RAM: 64GB system memory
• Storage: NVMe SSD for optimal data loading

Enterprise Configuration

• GPU: 40-48GB VRAM (NVIDIA A100 or H100)
• RAM: 128GB+ system memory
• Multi-GPU setup for larger batch sizes

CPU Training

• Not viable - would take days to weeks for single epoch
• GPU absolutely required for ViT Large

Common Issues and Solutions

Out of Memory Errors

Problem: CUDA out of memory, even with small batch size

Solutions:

1. Reduce batch_size to 2 (minimum viable)
2. Enable gradient checkpointing if available
3. Use gradient accumulation (effective batch size without memory cost)
4. Reduce image resolution to 192x192 or 160x160
5. Use mixed precision training (FP16)
6. Consider switching to ViT Base

Severe Overfitting

Problem: Large gap between training (high) and validation (low) accuracy

Solutions:

1. Reduce model complexity - switch to ViT Base
2. Collect significantly more training data
3. Reduce epochs by 50%
4. Lower learning rate to 1e-5 or 2e-5
5. Apply aggressive data augmentation
6. Add dropout or weight decay if configurable

Extremely Slow Training

Problem: Each epoch takes hours or training doesn't progress

Solutions:

1. Verify GPU is being used (check nvidia-smi)
2. Increase batch_size if memory allows
3. Enable mixed precision training
4. Use faster data loading (multiple workers, prefetching)
5. Ensure data is on fast storage (SSD/NVMe)
6. Consider ViT Base for faster iteration

Poor Convergence

Problem: Loss decreases very slowly or plateaus early

Solutions:

1. Increase learning rate to 1e-4 (carefully)
2. Ensure sufficient training data (>10k images)
3. Check data augmentation isn't too aggressive
4. Verify batch normalization is working correctly
5. Try longer training (more epochs)
6. Consider learning rate scheduling (warmup + decay)

Inconsistent Results

Problem: Validation accuracy varies significantly between runs

Solutions:

1. Increase batch_size for more stable gradients
2. Use more epochs to allow proper convergence
3. Set random seeds for reproducibility
4. Check for data leakage between train and validation
5. Ensure validation set is sufficiently large (10-20% of data)

Example Use Cases

Large-Scale Product Classification

Scenario: Classifying 100,000 e-commerce products into 500 categories

Configuration:

Model: ViT Large
Batch Size: 16
Epochs: 3
Learning Rate: 5e-5
Images: 100,000 products (200 per category average)
GPU: NVIDIA A100 40GB

Why ViT Large: Massive dataset, many fine-grained categories, need maximum accuracy, have GPU resources

Expected Results: 88-92% top-1 accuracy, 96-98% top-5 accuracy

Medical Imaging Diagnosis

Scenario: Multi-class disease classification from retinal scans

Configuration:

Model: ViT Large
Batch Size: 8
Epochs: 5
Learning Rate: 2e-5
Images: 25,000 retinal images (15 disease categories)
GPU: NVIDIA RTX 4090 24GB

Why ViT Large: Critical accuracy requirements, complex medical imaging patterns, sufficient data available

Expected Results: 93-96% accuracy with proper data quality and expert labeling

Fine-Grained Species Classification

Scenario: Identifying 200 bird species from photographs

Configuration:

Model: ViT Large
Batch Size: 12
Epochs: 8
Learning Rate: 3e-5
Images: 40,000 bird images (200 per species)
GPU: NVIDIA A6000 48GB

Why ViT Large: Subtle visual differences between species, need fine-grained feature learning, adequate data per class

Expected Results: 85-90% accuracy on challenging fine-grained classification

Comparison with Alternatives

ViT Large vs ViT Base

Choose ViT Large when:

• Dataset exceeds 10,000 images
• Maximum accuracy is critical
• Have 16GB+ GPU available
• Can afford longer training time
• Accuracy gain of 2-5% justifies cost

Choose ViT Base when:

• Dataset is 1,000-10,000 images
• Training time is important
• GPU memory is limited (8-16GB)
• Need faster iteration cycles
• Accuracy requirements are reasonable

ViT Large vs ResNet-101

Choose ViT Large when:

• Very large dataset (>20k images)
• Accuracy is paramount
• Global context is important
• Modern GPU infrastructure

Choose ResNet-101 when:

• Need faster training and inference
• Dataset is small to medium (<10k images)
• Limited GPU resources
• Deployment constraints favor smaller models
• Convolutional inductive bias is beneficial

ViT Large vs EfficientNet-B0

Choose ViT Large when:

• Maximum accuracy needed
• Large dataset available
• Computational resources abundant
• Research or competition setting

Choose EfficientNet-B0 when:

• Efficiency is critical
• Deployment to resource-constrained environments
• Smaller dataset (<10k images)
• Need balance of accuracy and size
• Inference speed matters