PyTorch Classification

[Flyheap]

Key Objectives:

Model Performance: We aim to enhance the performance of PyTorch models for classification. This includes improvements in model architectures, loss functions, and training strategies to achieve state-of-the-art results across a variety of classification tasks.

Data Handling: High-quality data is crucial for classification. We will address data preprocessing, augmentation, and loading techniques to streamline the process of preparing datasets for classification experiments.

Transfer Learning: Leveraging pre-trained models for classification tasks is common practice. This issue will focus on facilitating transfer learning with PyTorch, enabling users to adapt pre-trained models to new classification challenges.

Customization and Flexibility: We recognize that classification tasks can be diverse. We will work on making PyTorch more customizable and adaptable to the unique requirements of different classification problems.

Documentation and Resources: To support users at all skill levels, we will improve the documentation related to classification, provide tutorials, and create code examples to help users understand and implement classification tasks effectively.

Issue Resolution: If there are known issues, bugs, or limitations related to classification in PyTorch, this issue serves as a platform to identify, discuss, and address these challenges.

[Lakshya-GG]

can you assign me this issue @Flyheap

[Flyheap]

ISSUE ASSIGNED @Lakshya-GG