Image ClassificationTutorial

Implement powerful image classification using AI Dino's state-of-the-art models

Getting Started

Installation

Install our core library to access image classification capabilities. 

npm install @ai-dino/core

First Project

Use our pre-trained image classifier for fast results. 

import { ImageClassifier } from '@ai-dino/core'

const classifier = new ImageClassifier('resnet-200')

Step-by-Step Implementation

1. Model Configuration

// Choose a pre-built model
const model = new ImageClassifier({
modelType: 'resnet-200',
numClasses: 1000,
pretrained: true
})

Available models: resnet-50, efficientnet-b3, vision-transformer, custom.

2. Training Your Model

// Prepare dataset
const dataset = new ImageDataset('path/to/images', {
batch_size: 32
})

// Train model
await model.train(dataset, {
epochs: 20,
learning_rate: 0.001
})

Our system includes auto-hyperparameter optimization and data augmentation.

3. Making Predictions

const results = classifier.predict([
'./test-image-1.jpg',
'./test-image-2.jpg'
])

console.log(results) // Returns top-5 predictions

Model returns confidence scores for each prediction category.

4. Performance Optimization

Speed Tips
  • Use mixed-precision training
  • Enable model quantization
  • Utilize GPU acceleration
Accuracy Enhancements
  • Apply data augmentation
  • Use transfer learning
  • Implement model ensembling

Best Practices

Data Preparation

Ensure balanced class distribution and high-quality images (256x256+ resolution recommended)

Model Tuning

Experiment with learning rates between 1e-4 and 1e-3 for optimal convergence

Evaluation

Monitor validation loss and accuracy metrics during training

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