PyTorch Computer Vision

[Flyheap]

Key Objectives:

Model Architectures: We aim to provide a diverse set of state-of-the-art computer vision model architectures in PyTorch, covering tasks like image classification, object detection, and image segmentation. These models should be well-documented, easily accessible, and optimized for performance.

Data Handling: Computer vision tasks rely heavily on high-quality data. We will focus on improving data loading, augmentation, and preprocessing tools to simplify the process of working with image and video data.

Transfer Learning: Transfer learning is a cornerstone of computer vision. We'll enhance PyTorch's capabilities for fine-tuning pre-trained models on new computer vision tasks, making it simpler to adapt models to specific requirements.

Visualization Tools: To aid in model understanding and debugging, we will develop or recommend tools for visualizing model inputs, outputs, and intermediate features.

Customization and Fine-Tuning: Computer vision projects often require customization. We will work on making PyTorch more flexible and adaptable to users' unique requirements, allowing for the development of specialized vision models.

[Lakshya-GG]

can you assign me this issue @Flyheap

[Flyheap]

ISSUE ASSIGNED @Lakshya-GG