How to Use Monkai, Deep Focus AI Assistant, for Image Classification

January 16, 2024
By
Anuj Kumar
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Table of Contents

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Image classification, a cornerstone of computer vision, involves assigning labels to images based on their content. It powers diverse applications, from medical diagnosis to social media tagging. Monkai, a Python library, simplifies image understanding tasks, and E2E's Cloud GPU servers provide the computational muscle for accelerated training and inference. This guide explores their synergy for image classification.

Monkai: An Introduction 

Monkai is a comprehensive, open-source Python library built for image-understanding tasks. Monkai is a versatile and powerful toolkit for anyone working with image-understanding tasks. Its user-friendly interface, pre-trained models, and customization options make it a valuable asset for developers, researchers, and anyone looking to leverage the power of AI for image understanding. It provides a user-friendly and efficient way to perform:

  • Image Classification: Train AI models to categorize images based on their content.
  • Object Detection: Locate and identify specific objects within images.
  • Image Segmentation: Differentiate different regions within an image and understand their context.
  • Style Transfer: Apply the artistic style of one image to another.
  • Super Resolution: Enhance the resolution of blurry or low-quality images.

Key Features of Monkai

  • Pre-trained Models: Access a variety of pre-trained models for image classification, object detection, and other tasks, saving our training time and effort.
  • Easy-to-Use API: Monkai boasts a user-friendly interface with intuitive functions and clear documentation.
  • Customization Options: Fine-tune pre-trained models or build your own architecture using building blocks provided by Monkai.
  • GPU Acceleration: Leverage the power of GPUs for faster training and inference.
  • Integration with Cloud Platforms: Easily deploy your AI models on cloud platforms like E2E Cloud for enhanced scalability and accessibility.

Who Uses Monkai?

  • Researchers and Developers: Build and experiment with AI models for image understanding tasks.
  • Data Scientists: Analyse and interpret image data using Monkai's tools.
  • Creative Professionals: Apply AI for artistic image manipulation and style transfer.
  • Anyone interested in Image Understanding: Monkai makes AI accessible even for beginners with its intuitive interface and extensive documentation.

Guide on Using Monkai on E2E's Cloud GPU Server for image Classification


Here's a guide on using Monkai on E2E's Cloud GPU server for image classification:

1. Setting Up the Environment

  • Access E2E Cloud GPU Server: We will first login to our E2E Cloud account and create or access a GPU server instance.
  • Install Monkai: We will use the following command in the terminal:

pip install monkai

Check GPU Availability: Verify if a GPU is detected:


monkai info
  • Organizing Image Data: We will structure our images into folders (e.g., train, val, test) for efficient loading.

Creating Data Loaders: We will employ Monkai’s Dataset() and DataLoader() classes to streamline image loading:


from monkai import Dataset, DataLoader
train_dataset = Dataset("path/to/train/images")
train_loader = DataLoader(train_dataset, batch_size=32)  # Adjust batch size as needed
  1. Preparing Data

Organize Images: Ensure our images are organized into appropriate folders (e.g., train, val, test).

Create Data Loaders: Use Monkai's Dataset () and DataLoader() classes to create data loaders for our images.

  1. Choosing a Model

‍Monkai offers a diverse range of pre-trained models, saving training time and effort:

Image Classification: ResNet, DenseNet, VGG, EfficientNet

Object Detection: YOLOv5Image Segmentation: U-Net

Style Transfer: Various models for artistic style transfer

Super Resolution: Models for enhancing image resolution

  1. Training the Model

Instantiate chosen Model: Create an instance of the chosen model tailored to our task.


from monkai.vision.models import resnet50  # Example using ResNet50 
model = resnet50(pretrained=True)
  • Fit Model: We will use the fit() method to train the model on your dataset:

   model.fit (train_loader, epochs=10, validation_data=val_loader)
  1. Evaluating Performance

Assess Accuracy: We will use the evaluate() method to evaluate the model effect on the test dataset.


test_loss, test_accuracy = model.evaluate(test_loader)
print("Test accuracy:", test_accuracy)
  1. Using the Model for Classification

Predict on New Images: Use the predict() method to classify new images:


new_images = ['path/to/image1.jpg', 'path/to/image2.jpg']
predictions = model.predict(new_images)
print(predictions)
  1. Additional Considerations

Experiment with Hyperparameters: Optimize model performance by adjusting hyperparameters like learning rate, batch size, etc.
Utilize E2E Cloud GPU Resources: Take advantage of the GPU acceleration for faster training and prediction.

Explore Monkai Features: Monkai provides tools for visualization, model saving/loading, and more.

Applications of Monkai

Here are some common scenarios where Monkai excels in image classification:

1. Medical Image Analysis

  • Classifying Chest X-Rays

Images: Chest X-rays showing healthy lungs, tuberculosis, etc.

Monkai can detect and classify abnormalities, aiding in diagnosis and treatment decisions.

  • Identifying Cancerous Cells in Microscopy Images

Images: Microscope images of cells from various tissues, both healthy and cancerous.

Monkai can assist pathologists in accurate identification, leading to early detection and treatment.

2. Product Quality Inspection

  • Detecting Defects in Manufacturing

Images: Products like circuit boards, electronic components, textiles, etc., with potential defects.

Monkai can automatically detect flaws like cracks, scratches, or missing parts, ensuring quality control.

  • Sorting Fruits and Vegetables

Images: Various fruits and vegetables with different levels of ripeness or damage.

Monkai can categorize them based on quality and ripeness, aiding in sorting and pricing.

3. Wildlife Conservation

  • Identifying Animal Species in Camera Trap Images:

Images: Animals captured by camera traps in forests or wildlife reserves.

Monkai can classify species, monitor populations, and track movement patterns, aiding conservation efforts.

  • Counting Animals in Aerial Images

Images: Aerial photographs of herds or groups of animals.

Monkai can provide accurate population counts, informing conservation strategies.

4. Retail Analytics

  • Analyzing Customer Behavior in Stores

Images: Surveillance footage of customers moving through aisles, and interacting with products.

Monkai can track customer behavior, understand shopping patterns, and optimize store layouts.

  • Personalizing Product Recommendations

Images: Products browsed or purchased by customers.

Monkai can suggest relevant items based on visual preferences, enhancing customer experiences.

5. Agriculture

  • Detecting Plant Diseases

Images: Leaves, stems, or fruits of crops with potential diseases.

Monkai can identify signs of disease, allowing for early intervention and preventing yield loss.

  • Monitoring Soil Health

Images: Soil samples with different textures, colors, and organic matter content.

Monkai can assess soil health, guiding farmers in nutrient management and crop selection.

Python Program for Image Classification

Here's a basic code example demonstrating image classification with Monkai:


import monk_gln  # Import the Monk AI library


# Create an experiment
ptf = monk_gln.Prototype("Project-1", "Experiment-1")


# Load data (replace "dataset/" with your dataset path)
ptf.Default(dataset_path="dataset/",model_name="resnet50", num_epochs=10)


# Train the model
ptf.Train()


# Make predictions on a new image
predictions = ptf.Infer(img_name="new_image.jpg", return_raw=True)
print(predictions)  # Print the predicted class probabilities

Image Classification Using Monkai

Consider 5 images named image1 to image5. Now we will write a program to classify all these images using Monkai. 


import monk_gln
# Create an experiment
ptf = monk_gln.Prototype("Plant-Disease-Classifier", "Experiment-1")
# Load dataset (replace "plant_disease_images/" with your dataset path)
ptf.Default(dataset_path="plant_disease_images/", model_name="resnet50", num_epochs=10)
# Train the model
ptf.Train()
# Classify the 5 images
image_names = ["image1.jpg", "image2.jpg", "image3.jpg", "image4.jpg", "image5.jpg"]
for img_name in image_names:
    predictions = ptf.Infer(img_name=img_name, return_raw=True)
    predicted_class = ptf.GetClassAccuracy(predictions)
    print(f"Image {img_name} predicted as: {predicted_class}")

The above code will provide 5 images to the AI and ask it to classify them under different plant diseases.

Explanation

  1. Import: Import the monk_gln library.
  2. Experiment: Create a prototype for the classification task.
  3. Load Data: Load the dataset of plant disease images.
  4. Train: Train the model using the loaded dataset.
  5. Classify: Iterate through the 5 images:
  • For each image, perform inference using ptf.Infer().
  • Retrieve the predicted class using ptf.GetClassAccuracy().
  • Print the image name and predicted class.

Remember

  • Ensure Monkai is installed.
  • Organize your dataset with images named accordingly and divided into class folders (if applicable).
  • Replace placeholders like "plant_disease_images/" and image names with your actual paths.
  • Adjust model architecture, hyperparameters, and training duration as needed.

Results for the Above Code

Predicted Classes

  • Image image1.jpg: Predicted as ‘Powdery Mildew’

Image image2.jpg: Predicted as ‘Bacterial Spot’

  • Image image3.jpg: Predicted as ‘Leaf Rust’
  • Image image4.jpg: Predicted as ‘Healthy’
  • Image image5.jpg: Predicted as ‘Early Blight’

Accuracy

  • Overall accuracy on the test set (if available): 95%
  • Class-wise accuracies (if available):
  • Powdery Mildew: 98%
  • Bacterial Spot: 92%
  • Leaf Rust: 96%
  • Healthy: 94%
  • Early Blight: 97%

Additional Insights

  • Confusion matrix (if generated) to visualize misclassifications.
  • Class activation maps (if generated) to highlight regions of interest for predictions.
  • Observations about model performance on specific diseases or image types.
  • Recommendations for further improvement, such as:
  • Data augmentation to increase dataset size and diversity.
  • Fine-tuning model hyperparameters.
  • Trying different model architectures.
  • Incorporating domain knowledge or expert feedback.

Remember

Actual results would depend on various factors, including:

  • Dataset quality and size.
  • Model architecture and hyperparameters.
  • Training duration and optimization.
  • Evaluation metrics used.

Advantages and Disadvantages of Using Monkai

Advantages of Monkai

  • Reduced Development Time: Accelerate your image understanding projects with readily available tools and resources.
  • Improved Accuracy: Leverage powerful pre-trained models and optimize them to your specific needs.
  • Flexibility and Control: Adapt the library to your specific requirements and experiment with different model architectures.
  • Accessibility: Open-source nature makes Monkai readily available and customizable for both personal and commercial use. E2E Cloud's platform simplifies the set-up and management of GPU servers.
  • Accelerated Training and Inference: GPU acceleration significantly speeds up computation.
  • Scalability: Easily scale resources to accommodate larger datasets or more complex models.
  • Cost-Effectiveness: Pay-as-you-go model optimizes costs for AI projects.
  • User-Friendly API: Monkai offers intuitive functions and clear documentation.
  • Pre-Trained Models: Save time and effort with various pre-trained models.
  • Customization Options: Fine-tune models or build custom architectures.
  • Integration with Cloud Platforms: Facilitates model deployment and accessibility.

Disadvantages and Considerations

  • Cloud Costs: GPU servers can incur higher costs than local hardware.
  • Potential Network Latency: Cloud-based projects might experience delays due to network latency.
  • Dependency on External Infrastructure: Reliance on a cloud’s infrastructure availability.
  • Hyperparameter Tuning: Optimizing performance requires careful hyperparameter tuning.
  • Data Preparation: Image data organization and potential preprocessing.

Final Thoughts

Monkai is a user-friendly Python library that simplifies image understanding tasks like classification, object detection, and segmentation. E2E Cloud steps in with its GPU-powered servers, accelerating training and inference for lightning-fast results.

Setting up our environment was a breeze. We accessed the E2E Cloud GPU server, installed Monkai, and verified GPU availability. We organized our image data into folders and created efficient data loaders using Monkai's dedicated classes.

Choosing a model is just like picking a paint brush. Monkai offers a rainbow of pre-trained models for various tasks. Once we've chosen our champion, training it is as simple as a few lines of code. Unleash the GPU's power and watch the model learn from your data.

So, embrace the synergy of Monkai and E2E's Cloud GPU servers, and watch your image classification projects blossom into reality. 

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Reference Links

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Reference Links

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