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generate augmented images to increase the size of the training dataset for image classification tasks. This can involve applying various transformations like rotation, translation, scaling, or adding noise to existing images.
augment an existing dataset for image classification tasks. Increasing the dataset size will help in projects where the size of the dataset is very less. Also it can help in reducing bias and variance.
Scope
This project is helpful in enhancing the performance and generalization of image classification models by increasing the size and diversity of the training dataset through data augmentation. It allows the model to learn to be more robust and invariant to real-world variations and transformations, ultimately improving its accuracy and reliability in practical applications.
Timeline
I may take 1 Week for completing this project as my Sem End exams are coming up. Hope I will learn a lot from doing this. Here is a rough timeline of the project.
Day 1: Dataset preparation.
Day 2: Model selection and training.
Day 3: Augmentation generation.
Day 4: Dataset augmentation.
Day 5: Training and evaluation.
Day 6: Fine-tuning and iteration.
Day 7: Documentation and reporting
Project Request
Anantashayana
Define You
Project Name
Data Augmentation for Image Classification
Description
augment an existing dataset for image classification tasks. Increasing the dataset size will help in projects where the size of the dataset is very less. Also it can help in reducing bias and variance.
Scope
This project is helpful in enhancing the performance and generalization of image classification models by increasing the size and diversity of the training dataset through data augmentation. It allows the model to learn to be more robust and invariant to real-world variations and transformations, ultimately improving its accuracy and reliability in practical applications.
Timeline
I may take 1 Week for completing this project as my Sem End exams are coming up. Hope I will learn a lot from doing this. Here is a rough timeline of the project. Day 1: Dataset preparation. Day 2: Model selection and training. Day 3: Augmentation generation. Day 4: Dataset augmentation. Day 5: Training and evaluation. Day 6: Fine-tuning and iteration. Day 7: Documentation and reporting