#state-of-the-art

Det3D is a 3D Object Detection toolbox providing ready-to-use implementations of algorithms like PointPillars and SECOND. It supports benchmarks such as KITTI and nuScenes and includes features like multi-dataset support, a variety of models, distributed training, SyncBN, TensorboardX, and flexible anchor dimensions, thus supporting research in 3D object detection.

Mantis enhances multi-image visual language task processing using LLaMA-3, allowing for efficient handling of text and image inputs simultaneously. By achieving top performance on five key benchmarks with minimal resources, Mantis stands out without extensive pre-training data. It ensures robust single-image capabilities similar to CogVLM and Emu2. Recent updates include Idefics-3 training support and evaluation tools via VLMEvalKit. Explore a variety of models and scripts available on Hugging Face.

VisionLLaMA is a unified vision transformer similar to LLaMA, optimized for various image tasks including perception and generation. It consistently outperforms previous state-of-the-art models and serves as a strong benchmark for vision-related tasks. This model has been validated through typical pre-training methods, proving its efficacy and adaptability in processing 2D images. Designed to set new standards in the field of vision tasks without exaggeration.

Explore co-occurrence guided region-word alignment in open-vocabulary object detection using large-scale image-text data. CoDet achieves leading performance on datasets such as LVIS and COCO and integrates with tools like Roboflow for efficient automated labeling. Access details on installation, model setup, and training to enhance detection accuracy and efficiency.