Introduction

This post walks through a practical, production-minded computer vision pipeline that processes 40,000+ images and reaches 92% classification accuracy.

Architecture

• Data ingestion and validation
• Preprocessing and augmentation
• Transfer learning with ResNet-50
• Feature reduction (PCA) where it helps
• Training, evaluation, and monitoring hooks

Implementation (simplified)

import torch
import torchvision.models as models

model = models.resnet50(weights=models.ResNet50_Weights.DEFAULT)
model.fc = torch.nn.Linear(2048, num_classes)

Results

• 92% classification accuracy
• 0.08 validation loss
• Strong generalization across multiple visual conditions