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An Image Classification project w/ MobileNetV2 and DenseNet-121. Leveraging techniques like Hyperparameter Tuning, Transfer Learning, Imagine Preprocessing Techniques and Ensemble Methods.

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Bernardbyy/CIFAR10-ImageClassfication

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CIFAR10-ImageClassfication 🖼️

An Image Classification project utilizing MobileNetV2 and DenseNet-121. This project leverages advanced techniques to enhance model performance and accuracy, including:

Index Technique Description Specific Details
1 Cross Validation Utilized to ensure that the model generalizes well to new data. 5-fold cross-validation applied.
2 Early Stopping To prevent overfitting by stopping training when validation metrics stop improving. Patience level set to 5 epochs.
3 Transfer Learning Applying knowledge gained from one problem to a different but related problem. Leveraging pre-trained weights.
4 Hyperparameter Tuning Systematically searching for the optimal parameters of a model. Tuning epoch, batch size, and learning rate.
5 Data Augmentation Increasing the diversity of data available for training models without actually collecting new data. Includes random rotation, Gaussian blur, resizing, and bicubic interpolation.
6 Changing Optimization Algorithms Experimenting with different optimizers to improve training performance. Switch from Adam to AdamW optimizer.
7 Weighted Class Training Adjusting the importance of a class based on its weight to address class imbalance. More emphasis on classes like cats, dogs, birds, and planes.
8 Ensemble Methods Combining predictions from multiple models to improve accuracy. Soft voting ensemble of MobileNetV2 and DenseNet-121 logits.

This table provides an at-a-glance overview of the methodologies and specific adaptations made to optimize the CIFAR10 Image Classification project.

Overview of entire project: CIFAR-10 Image Classification Presentation

View the Full Report here: Full Report

Full project at: Full Source Codes (1.0 to 3.2)

Ablation Study:
7 iteration of MobileV2 Settings 📱:

Enhancements Approaches 1.0 1.1 2.0 2.1 2.2 2.3 2.4
Increasing Epochs
Bicubic Interpolation
Random Rotation
Gaussian Blur
Transfer Learning
Decreased Learning Rate
Increased Batch Size
AdamW Optimization
Weighted Class Training

2 iteration of DenseNet-121 Settings:

Enhancements Approaches 3.0 3.1
Increase Epochs
Increase Batch Size
Weighted Class Training
Transfer Learning
Decreased Learning Rate

Ensemble Results using Soft Voting (2.4 + 3.1) :

Model F1-Scores Accuracy
Train Test Improvement Train Test Improvement
2.4 0.9442 0.8874 - 0.9430 0.8842 -
3.1 0.9802 0.9235 +0.0361 0.9802 0.9234 +0.0392
3.2 (2.4+3.1) 0.9859 0.9342 +0.0107 0.9859 0.9341 +0.0107

Ensemble Method Results across individual classes :
F1-Score: image

Precision: image

Recall: image

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An Image Classification project w/ MobileNetV2 and DenseNet-121. Leveraging techniques like Hyperparameter Tuning, Transfer Learning, Imagine Preprocessing Techniques and Ensemble Methods.

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