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Model Building Stages

1. Define the Problem​

Clearly define the goal of the project: to build a CV model that detects aflatoxin contamination levels in corn samples through image analysis. The contamination levels will be categorized into predefined bands such as 0-30 ppb, 31-50 ppb, etc.

  • Output: Classification of aflatoxin levels into one of the specified categories.
  • Performance Target: Achieve at least 80% accuracy in classifying contamination levels.

2. Collect and Label Data​

The success of a CV model depends heavily on the quality and quantity of data:

  • Image Dataset: Obtain a dataset of corn images provided by the client, with images labeled based on the aflatoxin contamination levels.
  • Data Labels: Ensure that each image has a label that specifies the contamination level (in ppb). These will serve as the ground truth for training the model.
  • Data Size: Ensure the dataset is large enough to prevent overfitting. If the dataset is small, consider techniques like data augmentation to artificially increase the dataset size.

3. Preprocess the Data​

Preprocessing the images is essential to standardize the input data for the model:

  • Normalization: Scale pixel values to a range of [0, 1] or [-1, 1] to help the model converge faster.
  • Resizing: Resize all images to a fixed resolution (e.g., 224x224 pixels) to ensure consistency in input size.
  • Augmentation: Apply image augmentation techniques (e.g., rotation, flipping, zoom, brightness adjustments) to make the model more robust to variations in real-world conditions.
  • Train-Validation Split: Split the dataset into training and validation sets (e.g., 80% training, 20% validation) to evaluate model performance during development.

4. Choose a Model Architecture​

For image classification tasks, Convolutional Neural Networks (CNNs) are the most commonly used architectures:

  • Pre-trained Models (Transfer Learning):
    • Use pre-trained models like ResNet, MobileNet, or EfficientNet to leverage knowledge from large datasets like ImageNet. This can reduce training time and improve accuracy.
    • Transfer Learning: Fine-tune the pre-trained model on your specific dataset by replacing the final layer(s) to output the aflatoxin contamination categories.
  • Custom CNN Architecture:
    • If transfer learning isn’t sufficient, a custom CNN architecture can be built. Design layers that fit the complexity of your data, including convolutional layers, pooling layers, and fully connected layers.

5. Train the Model​

Now that the data is prepared and the model architecture is selected, proceed to training:

  • Loss Function: Use categorical cross-entropy as the loss function since this is a multi-class classification problem.
  • Optimizer: Use optimizers like Adam or SGD with momentum to adjust learning rates and improve convergence.
  • Batch Size & Epochs: Experiment with different batch sizes (e.g., 32, 64) and run multiple epochs (e.g., 50-100 epochs). Monitor overfitting using early stopping techniques.
  • Hyperparameter Tuning: Fine-tune hyperparameters like learning rate, dropout rate, and number of layers to optimize performance.

6. Evaluate the Model​

After training, evaluate the model to ensure it meets the desired performance criteria:

  • Confusion Matrix: Generate a confusion matrix to analyze how well the model performs across all contamination bands (e.g., 0-30 ppb, 31-50 ppb).
  • Performance Metrics: Evaluate key metrics like accuracy, precision, recall, F1-score for each class. For imbalanced datasets, consider using weighted precision/recall.
  • Cross-Validation: Perform k-fold cross-validation to ensure that the model generalizes well across different subsets of the data.

7. Improve the Model​

If the model does not meet the performance goals, several techniques can be used to improve it:

  • Data Augmentation: Further enhance the dataset by introducing more variability in the training data.
  • Model Regularization: Use techniques like dropout, batch normalization, or L2 regularization to prevent overfitting.
  • Hyperparameter Tuning: Use methods like grid search or random search to find optimal values for hyperparameters (e.g., learning rate, batch size).
  • Ensemble Methods: Combine multiple models (e.g., bagging or boosting) to improve prediction accuracy.

8. Test the Model​

Once the model is fine-tuned and evaluated, test its performance on a holdout test set or new data provided by the client:

  • Validation on New Data: Use unseen images from the client’s dataset to ensure that the model generalizes well to real-world samples.
  • Performance Metrics Report: Document the model's final accuracy, confusion matrix, and other performance metrics.

9. Deploy the Model (For POC)​

For the POC phase, the model will be deployed in a hosted environment (cloud or on-prem):

  • Deploy on Vendor's Environment: Host the model on a cloud server (e.g., AWS, Azure) where it can accept image inputs and return aflatoxin contamination levels via an API.
  • Performance Monitoring: Set up tools to monitor inference time, model accuracy, and resource utilization to ensure smooth operation.

10. Document and Report Results​

After deployment, prepare a comprehensive report to present to the client:

  • POC Results: Include detailed results of the model’s performance (e.g., accuracy, confusion matrix).
  • Recommendations for Future Phases: Provide insights on how the model can be scaled and improved further in Phase 2 (e.g., mobile app integration, on-device inference).

Tools and Technologies for Each Step​

  1. Preprocessing & Data Augmentation:
    • Tools: OpenCV, Keras ImageDataGenerator, Albumentations
  2. Model Development:
    • Tools: TensorFlow, Keras, PyTorch (for building CNNs and transfer learning)
  3. Training & Optimization:
    • Optimizers: Adam, SGD
    • Techniques: Early stopping, learning rate scheduling
  4. Evaluation:
    • Tools: scikit-learn (for confusion matrices and performance metrics)
  5. Deployment:
    • Tools: AWS SageMaker, Azure ML, or Google AI Platform