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ML Model Architecture

Comprehensive overview of 12 machine learning models and their performance

Best Performing Model
Attention Fusion
85.2% accuracy

ResNet-50

vision

82.3%
Accuracy

Deep residual network with skip connections enabling training of very deep networks

Architecture
Convolutional Neural Network with residual blocks
Parameters
25.6M

Key Strengths

Skip connections prevent vanishing gradients
Excellent feature extraction
Well-established architecture

DenseNet-121

vision

83.1%
Accuracy

Dense connectivity pattern where each layer receives feature maps from all preceding layers

Architecture
Densely connected convolutional networks
Parameters
7.9M

Key Strengths

Parameter efficient
Strong feature reuse
Reduced overfitting

ConvNeXt V2

vision

84.7%
Accuracy

Modern ConvNet design inspired by Vision Transformers with improved training strategies

Architecture
Modernized convolutional architecture
Parameters
28.6M

Key Strengths

State-of-the-art CNN performance
Improved training stability
Better generalization

Vision Transformer

vision

83.8%
Accuracy

Pure transformer architecture applied to image classification with patch-based processing

Architecture
Transformer encoder with image patches as tokens
Parameters
86.6M

Key Strengths

Attention-based processing
Global context modeling
Scalable architecture

Swin Transformer

vision

84.2%
Accuracy

Hierarchical vision transformer with shifted windowing for efficient computation

Architecture
Hierarchical transformer with shifted windows
Parameters
28.3M

Key Strengths

Linear computational complexity
Hierarchical representations
Cross-window connections

BERT Base

nlp

79.2%
Accuracy

Bidirectional encoder representations from transformers for language understanding

Architecture
Bidirectional transformer encoder
Parameters
110M

Key Strengths

Bidirectional context
Pre-trained representations
Fine-tuning capability

BERT MiniLM-L12

nlp

78.8%
Accuracy

Distilled BERT model with reduced parameters while maintaining performance

Architecture
Distilled transformer encoder
Parameters
33M

Key Strengths

Compact model size
Fast inference
Good performance retention

RoBERTa Base

nlp

79.6%
Accuracy

Robustly optimized BERT with improved training methodology

Architecture
Optimized transformer encoder
Parameters
125M

Key Strengths

Improved training strategy
Better performance
Robust representations

Early Fusion

multimodal

83.9%
Accuracy

Concatenates image and text features early in the processing pipeline

Architecture
Feature concatenation + MLP classifier
Parameters
Variable

Key Strengths

Simple implementation
Joint feature learning
Good baseline performance

Late Fusion

multimodal

84.1%
Accuracy

Combines predictions from separate image and text models

Architecture
Separate encoders + prediction fusion
Parameters
Variable

Key Strengths

Modality-specific optimization
Interpretable decisions
Flexible weighting

Attention Fusion

multimodal

85.2%
Accuracy

Uses learned attention weights to optimally combine multimodal features

Architecture
Cross-modal attention mechanism
Parameters
Variable

Key Strengths

Optimal feature weighting
Best performance
Adaptive fusion
Best Performance

Random Forest

classical

76.4%
Accuracy

Ensemble of decision trees with random feature selection

Architecture
Ensemble of decision trees
Parameters
~1K trees

Key Strengths

Interpretable results
Handles mixed data types
Robust to overfitting

Performance Summary

85.2%
Best Accuracy
Attention Fusion
12
Models Trained
Multiple Architectures
200+
Training Hours
GPU Compute Time
3
Modalities
Vision + Text + Fusion

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