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I will create image classification model
About this Gig
Key Project Deliverables
- Dataset Preprocessing: Pipeline to normalize raw 32x32 RGB images into a [0.0,1.0] range and perform one-hot encoding on class targets using to_categorical.
- CNN Model Architecture: A sequential model featuring three convolutional blocks (32 and 64 filters with 3x3 kernels and ReLU activations) paired with 2x2 Max Pooling layers, followed by dense decision layers.
- Model Training: Configured with the Adam optimizer and Categorical Cross-Entropy loss across 10 epochs with real-time validation tracking.
- Performance Visualization: Includes custom Matplotlib functions (plot_training_history) to generate side-by-side plots for training vs. validation accuracy and loss.
- Adaptable Codebase: Designed to be easily retrained on custom image datasets by modifying input dimensions, network depth, or regularization techniques like Dropout and Batch Normalization.
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FAQ
What is image classification and how does it work?
Image classification is a computer vision task where a algorithm takes an image as input and assigns it a label from a predefined set of categories. Deep learning models learn patterns like edges, textures, shapes, and complex features to predict what the image represents.
What are common architecture choices for image classification?
Convolutional Neural Networks (CNNs): Models like ResNet, EfficientNet, and MobileNet extract spatial visual features using convolutional layers. * Vision Transformers (ViTs): These models split images into patches and process them using self-attention mechanisms, excelling on large dataset
How do I prepare image data before training?
* Normalization: Scale pixel values to a [0,1] or [−1,1] range. * Resizing: Standardize all images to the input dimension expected by the model (i.e. 224×224). * Data Augmentation: Apply random rotations, flips, and crops to reduce overfitting and make the model more robust to real-world variability
What loss functions and evaluation metrics should I use?
* Loss Functions: Categorical Cross-Entropy for multi-class classification (single label per image) and Binary Cross-Entropy for multi-label classification (multiple labels per image). * Metrics: Use Accuracy for balanced datasets. Use Precision/Recall or F1-Score if dataset suffers class imbalance
