#PyTorch Implementation

SparseBEV is an advanced project focusing on sparse 3D object detection using multi-camera footage, designed for high efficiency and accuracy. The official PyTorch implementation, recognized at ICCV 2023, showcases top-tier performance on the nuScenes dataset. Its unique sparse sampling method boosts processing efficiency without sacrificing accuracy. The project also offers a model zoo for diverse benchmarking needs, and features like SparseOcc and visualization tools make it highly beneficial for developers and researchers.

The project provides an improved PyTorch implementation for scalable diffusion models with transformers, focusing on optimizing training and memory efficiency. It features pre-trained class-conditional models on ImageNet (512x512, 256x256) and tools for both sampling and training. Enhancements like gradient checkpointing and mixed precision training lead to notable performance gains. Resources such as Hugging Face Space and Colab notebooks facilitate easy deployment and model training. Evaluation tools support metrics computation like FID and Inception Score for thorough analysis.

This open-source project leverages non-autoregressive TTS to enhance expressive voice synthesis, including emotional and conversational applications. It utilizes AIHub and IEMOCAP datasets to support multilingual processing, with a focus on English and Korean. Developers can easily adapt to other languages using the project's guidelines. Built on the FastSpeech2 framework, it facilitates multi-speaker synthesis with intricate emotional tones. The repository includes branches for emotional TTS using categorical and continuous descriptors, as well as conversational TTS incorporating dialogue history. This project serves as a vital tool for researchers and developers in the field of expressive voice synthesis.

This project introduces a PyTorch-based multi-branch network for accurate nuclear instance segmentation and classification in histology images. By utilizing pixel distances, it distinctly separates clustered cells and classifies nuclear types. The implementation supports processing tasks with model weights from datasets like CoNSeP, PanNuke, and more.

Discover the StyleGaussian project, a unique 3D style transfer system offering real-time transformations with full multi-view consistency. The PyTorch implementation uses Gaussian Splatting for effective style changes. Style transfers can be conducted through an interactive remote viewer or inference rendering with multiple styles. Detailed installation, quick start instructions, and training options—either from scratch or with pretrained models—ensure versatility in 3D artistic visualizations.