PyTorch Computer Vision #4
Description
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.