Image Classifier Block

A block that can be used for Image Classification using deep learning.

Overview

The Image Classifier block leverages deep learning models to automatically classify images into predefined categories. It's designed to handle various image types and provides accurate classification results with confidence scores.

Configuration Options

Model Configuration

Choose the classification model to use:

Pre-trained Models: Use existing trained models for common classification tasks
Custom Models: Upload and use your own trained models
Model Categories: Select from available model categories (objects, scenes, faces, etc.)

Classification Settings

Confidence Threshold: Minimum confidence score for classification results
Top-K Results: Number of top classification results to return
Batch Processing: Process multiple images simultaneously
Output Format: Choose output format (JSON, CSV, etc.)

Image Processing

Image Preprocessing: Apply preprocessing steps (resize, normalize, etc.)
Input Format: Supported image formats (JPEG, PNG, TIFF, etc.)
Color Space: Handle different color spaces (RGB, grayscale, etc.)
Image Quality: Optimize for different image qualities

How It Works

The Image Classifier block:

Receives Image: Gets image data from input message
Preprocesses Image: Applies necessary preprocessing steps
Runs Classification: Uses deep learning model to classify the image
Returns Results: Sends classification results with confidence scores

Classification Flow

Image Input → Preprocessing → Model Inference → Classification Results

Use Cases

Content Moderation

Classify images for content moderation:

image input → Image Classifier → classification → moderation decision

Medical Imaging

Classify medical images for diagnosis:

medical image → Image Classifier → diagnosis category → medical analysis

Quality Control

Classify products for quality control:

product image → Image Classifier → quality category → quality decision

Document Classification

Classify document types:

document image → Image Classifier → document type → routing

Common Patterns

Basic Image Classification

// Configuration
Model: Pre-trained Object Classification
Confidence Threshold: 0.8
Top-K Results: 3
Output Format: JSON

// Input: Image data
// Output: {
//   classifications: [
//     { label: "cat", confidence: 0.95 },
//     { label: "dog", confidence: 0.03 },
//     { label: "bird", confidence: 0.02 }
//   ]
// }

Batch Image Processing

// Configuration
Model: Custom Product Classification
Batch Processing: true
Batch Size: 10
Confidence Threshold: 0.7

// Input: Array of images
// Output: Array of classification results

Multi-Class Classification

// Configuration
Model: Scene Classification
Categories: ["indoor", "outdoor", "urban", "natural"]
Confidence Threshold: 0.6
Output Format: Structured

// Input: Scene image
// Output: {
//   primary_class: "outdoor",
//   confidence: 0.89,
//   all_classes: {
//     "outdoor": 0.89,
//     "natural": 0.78,
//     "urban": 0.12,
//     "indoor": 0.01
//   }
// }

Advanced Features

Custom Model Support

Upload and use custom trained models:

Model Upload: Upload your own trained models
Model Validation: Validate model compatibility and performance
Model Management: Manage multiple models for different tasks
Model Versioning: Track and manage model versions

Real-time Processing

Handle real-time image classification:

Streaming Support: Process image streams in real-time
Low Latency: Optimize for minimal processing delay
Scalability: Handle high-volume image processing
Resource Management: Efficient resource utilization

Advanced Analytics

Comprehensive classification analytics:

Confidence Analysis: Detailed confidence score analysis
Error Analysis: Identify and analyze classification errors
Performance Metrics: Track model performance over time
Bias Detection: Detect and report potential model bias

Configuration Examples

Object Detection and Classification

// Configuration
Model: YOLO Object Detection
Confidence Threshold: 0.7
Output Format: JSON with bounding boxes
Batch Processing: false

// Use case: Detect and classify objects in images

Medical Image Analysis

// Configuration
Model: Medical Image Classification
Categories: ["normal", "abnormal", "critical"]
Confidence Threshold: 0.9
Output Format: Medical report format

// Use case: Classify medical images for diagnosis

Content Moderation

// Configuration
Model: Content Moderation
Categories: ["safe", "unsafe", "review_required"]
Confidence Threshold: 0.8
Batch Processing: true

// Use case: Automatically moderate image content

Tips

Choose Appropriate Models: Select models that match your specific use case
Set Confidence Thresholds: Adjust thresholds based on your accuracy requirements
Preprocess Images: Ensure images are properly preprocessed for optimal results
Monitor Performance: Track classification accuracy and model performance
Handle Edge Cases: Consider how to handle unusual or problematic images
Optimize for Scale: Use batch processing for high-volume scenarios

Common Issues

Low Classification Accuracy

Issue: Poor classification results
Solution: Check image quality, preprocessing, and model selection

Slow Processing

Issue: Classification takes too long
Solution: Optimize image preprocessing and use batch processing

Memory Issues

Issue: Out of memory errors with large images
Solution: Implement image resizing and batch processing

Model Compatibility

Issue: Model not working with certain image formats
Solution: Check model requirements and image preprocessing

Performance Considerations

Model Selection

Accuracy vs Speed: Balance between classification accuracy and processing speed
Resource Requirements: Consider CPU/GPU memory and processing requirements
Model Size: Larger models may provide better accuracy but require more resources

Optimization Strategies

Image Resizing: Resize images to optimal dimensions for the model
Batch Processing: Process multiple images together for better efficiency
Caching: Cache model results for repeated classifications
Parallel Processing: Use multiple processing threads for better performance

Image Processor - For image preprocessing and enhancement
Object Detector - For detecting objects in images
OCR - For extracting text from images
debug - For monitoring classification results

Image Classifier

On this page