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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) |
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This repository includes GiT checkpoints, logs, and the pre-trained files used. |
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## Model Details |
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### Model Description |
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In this project, we introduce GiT (Large Visual Modeling). GiT has the following characteristics: |
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- 😮 **Minimalist architecture design similar to LLM**: GiT consists solely of a single transformer, without the inclusion of additional vision encoder and adapter. |
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- 🚀 **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). |
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- 🤗 **Achieving task synergy by unified language interface**: Similar to LLM, GiT observes task synergy effect in multi-task training. |
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- 🔥 **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. |
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- **Developed by:** Haiyang Wang ( [email protected] ), Hao Tan |
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pache license 2.0 |
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### Model Sources |
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- **Repository:** https://github.com/Haiyang-W/GiT |
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- **Paper:** https://arxiv.org/abs/2222.33333 |
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## Uses |
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Please refer [here](https://github.com/Haiyang-W/GiT) for more detail about usage. |
