Merge branch 'main' of https://huggingface.co/MILVLG/Imp-v0-3b into main

README.md

@@ -2,12 +2,12 @@
 license: apache-2.0
 pipeline_tag: visual-question-answering
 ---
-# 😈 IMP
+# 😈Imp
 
 
-The 😈 IMP project aims to provide a family of  a strong multimodal `small` language models (MSLMs). Our `IMP-v0-3B` model is a strong MSLM with only **3B** parameters, which is build upon a small yet powerful SLM [Phi-2 ](https://huggingface.co/microsoft/phi-2)(2.7B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on the [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) training set.  
+The Imp project aims to provide a family of  a strong multimodal `small` language models (MSLMs). Our `imp-v0-3b` is a strong MSLM with only **3B** parameters, which is build upon a small yet powerful SLM [Phi-2 ](https://huggingface.co/microsoft/phi-2)(2.7B) and a powerful visual encoder [SigLIP ](https://huggingface.co/google/siglip-so400m-patch14-384)(0.4B), and trained on the [LLaVA-v1.5](https://github.com/haotian-liu/LLaVA) training set.  
 
-As shown in the Table below, `IMP-v0-3B` significantly outperforms the counterparts of similar model sizes, and even achieves slightly better performance than the strong LLaVA-7B model on various multimodal benchmarks. 
+As shown in the Table below, `imp-v0-3b` significantly outperforms the counterparts of similar model sizes, and even achieves slightly better performance than the strong LLaVA-7B model on various multimodal benchmarks. 
 
 We release our model weights and provide an example below to run our model . Detailed technical report and corresponding training/evaluation code will be released soon on our [GitHub repo](https://github.com/MILVLG/imp). We will persistently improve our model and release the next versions to further improve model performance :) 
 
@@ -47,7 +47,7 @@ print(tokenizer.decode(output_ids[input_ids.shape[1]:], skip_special_tokens=True
 ```
 
 ## Model evaluation
-We perform evaluation on 8 commonly-used benchmarks to validate the effectiveness of our model, including 5 academic VQA benchmarks and 3 recent MLLM benchmarks.
+We perform evaluation on 9 commonly-used benchmarks to validate the effectiveness of our model, including 5 academic VQA benchmarks and 4 popular MLLM benchmarks.
 
 | Models | Size | VQAv2 | GQA |VisWiz  | SQA (IMG) | TextVQA | POPE |  MME | MMB  |MM-Vet|
 |:--------:|:-----:|:----:|:----:|:-------------:|:--------:|:-----:|:----:|:-------:|:-------:|:-------:|
@@ -56,11 +56,11 @@ We perform evaluation on 8 commonly-used benchmarks to validate the effectivenes
 | [LLaVA-Phi](https://github.com/zhuyiche/llava-phi) | 3B | 71.40  | - | 35.90 |    68.40   |    48.60  | 85.00 | 1335.1 | 59.80 |28.9|
 | [MobileVLM](https://huggingface.co/mtgv/MobileVLM-3B) | 3B | - | 59.00  | - |    61.00   |    47.50   | 84.90 | 1288.9 | 59.60  |-|
 | [MC-LLaVA-3b](https://huggingface.co/visheratin/MC-LLaVA-3b) | 3B | 64.24 | 49.6  | 24.88 |    -   |    38.59   | 80.59 | - | -  |-|
-| **IMP-v0 (ours)** | 3B | **79.45**  | 58.55 | **50.09** |**69.96**| **59.38** | **88.02**| 1434 | **66.49**  |**33.1**|
+| **Imp-v0 (ours)** | 3B | **79.45**  | 58.55 | **50.09** |**69.96**| **59.38** | **88.02**| 1434 | **66.49**  |**33.1**|
 
 
 ## License
 This project is licensed under the Apache License 2.0 - see the [LICENSE](https://www.apache.org/licenses/LICENSE-2.0) file for details.
 
 ## About us
-Project :smiling_imp: IMP is maintained by the [MILVLG](https://github.com/MILVLG) group led by Prof. Zhou Yu and Jun Yu, and mainly developed by Zhenwei Shao and Xuecheng Ouyang. We hope our model may server as a strong baseline to inspire future research on MSLMs and derivative applications on mobile devices and robotics. 
+This project is maintained by the [MILVLG](https://github.com/MILVLG)@Hangzhou Dianzi University (HDU) led by Prof. Zhou Yu and Jun Yu, and mainly developed by Zhenwei Shao and Xuecheng Ouyang. We hope our model may serve as a strong baseline to inspire future research on MSLM, as well as its derivative applications on mobile devices and robots.