Update README.md

README.md

@@ -24,16 +24,17 @@ pipeline_tag: visual-question-answering
 
 Human-related vision and language tasks are widely applied across various social scenarios.  The latest studies demonstrate that the large vision-language model can enhance the performance of various downstream tasks in visual-language understanding.  Since, models in the general domain often not perform well in the specialized field.  In this study, we train a domain-specific Large Language-Vision model, Human-LLaVA, which aim to construct an unified multimodal Language-Vision Model for Human-related tasks.
 
-Specifically, (1) we first construct a large-scale and high-quality human-related image-text (caption) dataset extracted from Internet for domain-specific alignment in the first stage (Coming soon);  (2) we also propose to construct a multi-granularity caption for human-related images (Coming soon), including human face, human body, and whole image, thereby fine-tuning a large language model.  Lastly, we evaluate our model on a series of downstream tasks, our Human-LLaVA achieved the best overall performance among multimodal models of similar scale.  In particular, it exhibits the best performance in a series of human-related tasks, significantly surpassing similar models and ChatGPT-4o.  We believe that the Huaman-LLaVA model and a series of datasets presented in this work can promote research in related fields.
+Specifically, (1) we first construct **a large-scale and high-quality human-related image-text (caption) dataset** extracted from Internet for domain-specific alignment in the first stage (Coming soon);  (2) we also propose to construct **a multi-granularity caption for human-related images** (Coming soon), including human face, human body, and whole image, thereby fine-tuning a large language model.  Lastly, we evaluate our model on a series of downstream tasks, our **Human-LLaVA** achieved the best overall performance among multimodal models of similar scale.  In particular, it exhibits the best performance in a series of human-related tasks, significantly surpassing similar models and ChatGPT-4o.  We believe that the Huaman-LLaVA model and a series of datasets presented in this work can promote research in related fields.
 
 
 ## Result
 
 ## News and Update 🔥🔥🔥
-* 2024.09.08 **🤗[Human_LLaVA_8B](https://huggingface.co/OpenFace-CQUPT/Human_LLaVA), is released!👏👏👏**
+* Sep.8, 2024. **🤗[Human-LLaVA-8B](https://huggingface.co/OpenFace-CQUPT/Human_LLaVA), is released!👏👏👏**
 
 
-## How to Use
+## 🤗 Transformers
+To use Human-LLaVA for the inference, all you need to do is to input a few lines of codes as demonstrated below. However, please make sure that you are using latest code.
 ``` python
 import requests
 from PIL import Image
@@ -67,6 +68,7 @@ print(predict)
 HumanCaption-10M(self construct): Coming Soon!
 
 #### Instruction Tuning Stage
+All public data sets have been filtered, and we will consider publishing all processed text in the future
 
 HumanCaptionHQ-300K(self construct): Coming Soon! 
 
@@ -76,13 +78,12 @@ humanvg_high_reg(self construct):Coming Soon!
 
 humanvg_high_rec(self construct):Coming Soon!
 
-celeba_attribute(self construct): 
+celeba_attribute(self construct): [CelebA](https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html)
 
-ShareGPT4V_caption: 
+ShareGPT4V:[ShareGPT4V]https://github.com/InternLM/InternLM-XComposer/blob/main/projects/ShareGPT4V/docs/Data.md
 
 LLaVA-Instruct_zh : 
 
-ShareGPT4V_vqa:  
 
 verified_ref3rec: