This repository includes GiT checkpoints, logs, and the pre-trained files used.
Model Details
Model Description
In this project, we introduce GiT (Generalist Vision Transformer). GiT has the following characteristics:
😮 Minimalist architecture design similar to LLM: GiT consists solely of a single transformer, without the inclusion of additional vision encoder and adapter.
🚀 Covering all types of visual understanding tasks: GiT addresses a spectrum of visual tasks, including object-level tasks (e.g., objecte detection), pixel-level tasks (e.g., semantic segmentation) and vision-language tasks (e.g., image captioning).
🤗 Achieving task synergy by unified language interface: Similar to LLM, GiT observes task synergy effect in multi-task training.
🔥 Strong performance on zero-shot and few-shot benchmark: GiT scales well with model size and data, demonstrating remarkable generalizability across diverse scenarios after trained on 27 datasets.
