Welcome to the AI Toolkit Image Recognition Examples page! 🐾🖼️ Here are some practical use cases and code snippets to help you get started with our API.

📌 Example 1: Object Detection in Daily Scenes

Detect objects like Cat, Dog, or Furniture in real-time videos.

Object Detection

✅ Sample Code (Python)

import ai_toolkit

# Initialize the image recognition model
model = ai_toolkit.ImageRecognitionModel("detect")

# Process a video stream
for frame in video_stream:
    results = model.analyze(frame)
    print(f"Detected: {results['objects']}")

📌 Example 2: Scene Classification

Identify Nature, Urban, or Industrial environments in images.

Scene Classification

🔍 How It Works

  1. Upload an image to the API endpoint /upload
  2. Use the classify method with the scene task
  3. Retrieve the predicted environment type

📌 Example 3: Custom Model Training

Train a model to recognize Custom_Object (replace with your own keyword)

Custom Object

🛠️ Training Pipeline

  • Prepare labeled datasets
  • Call /api/train with your model configuration
  • Download the trained model for inference

For more details on API parameters, visit our Image Recognition API Reference. 📚