Awesome GitHub RepositoriesMulti-Task Vision Training

MMF is a modular framework for building, training, and evaluating vision-and-language models. It provides a configuration-driven experiment system where model, dataset, and training parameters are defined through composable YAML files, alongside a curated model zoo of pretrained checkpoints for state-of-the-art multimodal architectures. The framework includes a multimodal dataset loader that downloads, processes, and batches vision-and-language data, and a vision-language model trainer supporting distributed training, mixed precision, and checkpoint-based resumption. The framework distinguish

UniAD 是一个用于自动驾驶的统一深度学习框架，将感知、预测和规划集成到一个端到端的模型中。它作为一种神经网络架构，将原始传感器数据直接映射为驾驶轨迹和运动规划。 本项目是规划导向方法的科研实现，联合训练占用空间、地图构建和目标跟踪模块。它采用多任务感知框架来优化整体驾驶性能。 该系统涵盖了广泛的功能，包括端到端驾驶流水线、车辆运动优化和视觉特征聚合。它协调各种自动驾驶任务，在单个训练周期内优化整个驾驶过程。

OmniGen 是一个统一的图像生成模型和扩散框架，通过单一系统处理文本、图像和视觉任务。它作为一个多模态扩散框架，利用共享模型权重将各种视觉操作视为统一的图像合成问题，无需外部适配器模块。 该系统支持主体驱动的图像生成，以保留参考照片中对象的身份，并允许进行多参考图像合成。它还可以作为基于指令的图像编辑器，通过自然语言提示修改视觉内容。 该框架扩展到生成式计算机视觉任务，通过将边缘检测和姿态识别等操作转换为合成任务来执行。特定任务的性能可以通过模型权重微调和低秩自适应 (LoRA) 来提升。

This project is a research framework and toolkit designed for training large-scale vision transformers and multimodal language models. It provides a comprehensive suite for vision-language pretraining, enabling the development of models that map images and text into shared latent spaces. The framework is distinguished by its capabilities in high-fidelity image generation and multimodal research, utilizing normalizing flows and variational autoencoders to produce images from text prompts or class labels. It supports the development of both generative and contrastive models, allowing for a wide