README.md

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license: apache-2.0
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# Model Card for Model ID

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[GiT: Towards Generalist Vision Transformer through Universal Language Interface](https://arxiv.org/abs/2222.33333)

This repository includes GiT checkpoints, logs, and the pre-trained files used.

## Model Details

### Model Description

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In this project, we introduce GiT (Large Visual Modeling). 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.
- 🔥 **SOTA 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.


![image/png](https://cdn-uploads.huggingface.co/production/uploads/6585493b53c37507639fe3ba/0-qINMmUF8ugjb2jdsHLa.png)

- **Developed by:** Haiyang Wang ( [email protected] ), Hao Tan

pache license 2.0

### Model Sources

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- **Repository:** https://github.com/Haiyang-W/GiT
- **Paper:** https://arxiv.org/abs/2222.33333

## Uses
Please refer [here](https://github.com/Haiyang-W/GiT) for more detail about usage.