diff --git a/PyTorch/built-in/mlm/PLLaVA/DATA.md b/PyTorch/built-in/mlm/PLLaVA/DATA.md
deleted file mode 100644
index cf7783763fa362e2c8e57fe78fbf354c3261485d..0000000000000000000000000000000000000000
--- a/PyTorch/built-in/mlm/PLLaVA/DATA.md
+++ /dev/null
@@ -1,124 +0,0 @@
-# Data
-## Instruction Training Data
-
-
-
-For training, we leveraged the video instruction tuning data from [Videochat2](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2).
-
-#### 1. Download json annotation files from huggingface.
-[](https://huggingface.co/datasets/OpenGVLab/VideoChat2-IT)
-
-
-
-#### 2. Download the raw videos from the following links.
-The video directories can be found in tasks/train/instruction_data.py. You can also change them to your own saved paths.
-
-- [VideoChat](https://github.com/OpenGVLab/InternVideo/tree/main/Data/instruction_data): Based on [InternVid](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid), download the processed version directly [here](https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/videochat2/data/videochat2_conversation_videos.zip)
-- [VideoChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT/tree/main/data)
-- [Kinetics-710](https://github.com/OpenGVLab/UniFormerV2/blob/main/DATASET.md), download Kinetics 400/600/700 [here](https://openxlab.org.cn/datasets?keywords=kinetics).
-- [SthSthV2](https://developer.qualcomm.com/software/ai-datasets/something-something): Option candidates were generated from [UMT](https://github.com/OpenGVLab/unmasked_teacher) top-20 predictions.
-- [NExTQA](https://github.com/doc-doc/NExT-QA)
-- [CLEVRER](https://clevrer.csail.mit.edu/)
-- [WebVid](https://maxbain.com/webvid-dataset/)
-- [YouCook2](https://youcook2.eecs.umich.edu/), download the processed version [here](https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/videochat2/data/youcook_split_videos.zip).
-- [TextVR](https://github.com/callsys/textvr)
-- [TGIF](https://github.com/YunseokJANG/tgif-qa)
-- [EgoQA](https://ego4d-data.org/), download the processed version [here](https://pjlab-gvm-data.oss-cn-shanghai.aliyuncs.com/videochat2/data/egoqa_split_videos.zip).
-
-#### 3. We also provide our processed json annotation files here.
-
-[](https://huggingface.co/datasets/cathyxl/magic_jsons)
-
-
-
-
-## Evaluation Data & Others
-Follow this section to obtain the evaluation open resources.
-
-### VCGBench
-
-We refer to the VideoChatGPT video question answering evaluation as VCGBench in this repo. We followed the original [repo](https://github.com/mbzuai-oryx/Video-ChatGPT/tree/main) to prepare the evaluation data.
-
-### MVBench
-We follow the original [Videochat2 repo](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2) in setting up the MVBench Evaluation. You can also find helpful resources at their [huggingface repo](https://huggingface.co/datasets/OpenGVLab/MVBench)
-
-
-### Videoqabench
-We refer to all other video question answering benchmarks as videoqabench in this repo. They are mainly prepared folloing the original repos. Each listed:
-1. [MSVD](https://www.cs.utexas.edu/users/ml/clamp/videoDescription/) & [MSRVTT](https://github.com/xudejing/video-question-answering)
-
-3. [Activity Net](https://github.com/MILVLG/activitynet-qa/tree/master)
-4. [TGIF](https://github.com/raingo/TGIF-Release/tree/master)
-
-Also other fantastic repo intergrating these benchmarks are helpful in the process of setting up the evaluation data:
-- [VideoChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT/tree/main)
-- [VideoLlava](https://github.com/PKU-YuanGroup/Video-LLaVA/tree/main/videollava)
-- [IG-VLM](https://github.com/imagegridworth/IG-VLM/tree/main)
-
-
-
-### Recaptioning
-#### Inter4k
-
-This is a dataset with 1000 samples of high resolution videos. We prepare the data folloing the instructions from their [official website](https://alexandrosstergiou.github.io/datasets/Inter4K/index.html)
-
-#### Extending Reacptioning
-The recaptioning part is designed to be extendable.
-
-inference script [tasks/eval/recaption/pllava_recaption.py](tasks/eval/recaption/pllava_recaption.py) would use a dataset class [RecaptionDataset](tasks/eval/recaption/__init__.py#L197). The detailed information is kept in the data_list_info attribute as:
-```
-data_list_info = OrderedDict({
- # "Panda70M": OrderedDict(
- # json_relpath="Panda70M/annotations.json",
- # prefix="DATAS/Recaption/Panda70M/videos",
- # data_type="video",
- # bound=False,
- # key_rename_map={
- # # 'caption': 'hint',
- # },
- # name_key='video_name',
- # postfix=('mp4', 'mkv', 'webm'),
- # recaption_type=RecaptionSample,
- # ), # don't has start & end
- "Inter4K": OrderedDict(
- json_relpath="Inter4K/annotations.json",
- prefix="DATAS/Recaption/Inter4K/60fps/UHD",
- data_type="video",
- bound=False,
- key_rename_map={
- # 'caption': 'hint',
- },
- name_key='video_name',
- postfix=('mp4', 'mkv', 'webm'),
- recaption_type=CaptionSample,
- ), # don't has start & end
- })
-```
-It contains the path to a annotation json file where there is a list and each item of the list is a sample waiting for captioning. For example, the Inter4K/annotations.json is like:
-```json
-[
- {
- "video_name": "973"
- },
- ...
-]
-```
-and the directory DATAS/Recaption/Inter4K/60fps/UHD would look like:
-```
-$ ls DATAS/Recaption/Inter4K/60fps/UHD
-1.mp4 134.mp4 170.mp4 ....
-```
-
-Naively, only the video is needed when captioning directly, therefore the annotation file only needs to contain the names of each video under the "prefix" directory.
-
-Extending a dataset for captioning would consist of the folloing steps:
-1. have all the videos downloaded
-2. construct a annotation.json file with sepecific format.
-3. configure the recaption dataset [here](tasks/eval/recaption/__init__.py#L197), where you would need to determine:
- - json_relpath: the annotation relative path
- - prefix: root directory for videos
- - postfix: a list containing all the file extensions for these videos
-
-The other options are experimental, so stick with the default setting as in Inter4k. The recommended length of video is around 5-20 seconds.
-
-p.s. "bound" is to make sure the video pass to the model doesn't have scene transition or so. This part wasn't tested, so set the bound to false and make sure the original videos files are single clip of a video. But always feel free to discover and contribute to PLLaVA!
\ No newline at end of file
diff --git a/PyTorch/built-in/mlm/PLLaVA/README.md b/PyTorch/built-in/mlm/PLLaVA/README.md
index b07bc8e8c546c10c547edffc0bcdc6bd9da6f13d..a856ec3fc38e9b84fdcec70cdd3bb048559f4356 100644
--- a/PyTorch/built-in/mlm/PLLaVA/README.md
+++ b/PyTorch/built-in/mlm/PLLaVA/README.md
@@ -1,372 +1,148 @@
-
-
+# PLLaVA for PyTorch
+# 目录
+
+- [简介](#简介)
+ - [模型介绍](#模型介绍)
+ - [支持任务列表](#支持任务列表)
+ - [代码实现](#代码实现)
+- [准备训练环境](#准备训练环境)
+ - [安装模型环境](#安装模型环境)
+ - [安装昇腾环境](#安装昇腾环境)
+ - [准备数据集](#准备数据集)
+ - [获取预训练模型](#获取预训练模型)
+- [快速开始](#快速开始)
+ - [模型训练](#模型训练)
+ - [结果展示](#结果展示)
+ - [模型推理](#模型推理)
+- [公网地址说明](#公网地址说明)
+- [变更说明](#变更说明)
+- [FAQ](#faq)
+
-[Lin Xu](https://scholar.google.com/citations?user=_Gu69coAAAAJ), [Yilin Zhao](https://ermu2001.github.io/me.io/), [Daquan Zhou](https://scholar.google.com/citations?user=DdCAbWwAAAAJ), [Zhijie Lin](https://scholar.google.com/citations?user=xXMj6_EAAAAJ), [See-Kiong Ng](https://scholar.google.com/citations?user=_wsommYAAAAJ), [Jiashi Feng](https://scholar.google.com.sg/citations?user=Q8iay0gAAAAJ&hl=en)
-
-
+ 请参考昇腾社区中《[Pytorch框架训练环境准备](https://www.hiascend.com/document/detail/zh/ModelZoo/pytorchframework/ptes)》文档搭建昇腾环境，本仓已支持表4中软件版本。
+
+
+ **表 4** 昇腾软件版本支持表
+ | 软件类型 | 支持版本 |
+ | :--------: |:--------:|
+ | FrameworkPTAdapter | 在研版本 |
+ | CANN | 在研版本 |
+ | 昇腾NPU固件 | 在研版本 |
+ | 昇腾NPU驱动 | 在研版本 |
+## 准备数据集
+- json文件下载路径参考： (https://huggingface.co/datasets/OpenGVLab/VideoChat2-IT)。
+- 视频文件下载参考：（https://github.com/magic-research/PLLaVA/blob/main/README.md 中的数据准备章节）。
+- 数据集结构如下所示：
+ ```
+ dataset/VideoChat2-IT/video/reasoning/clever_qa
+ ├── train.json
+
+ dataset/video_all
+ ├── xxx.mp4
+- 在训练脚本中（train_pllava_single_npu.sh（单卡）、 train_pllava_multi_npu.sh（单机多卡）、train_pllava_npu_multi_node.sh（多机多卡））通过指定train_corpus的value，在 tasks/train/instruction_data.py中获取具体的json路径和视频路径。
-## Overview
+## 获取预训练模型
-Welcome to PLLAVA!
+- 联网情况下，预训练模型会自动下载。
-The primary purpose of this repository is to support research and the development of prototype models. It is designed to facilitate ease of experimentation and enable a clear overview of results. Please note that this repo is currently undergoing development and reconstruction.
+- 无网络时，用户可访问huggingface官网自行下载，文件namespace如下：
+ 参考 https://github.com/magic-research/PLLaVA/blob/main/README.md 中的模型下载准备章节。
+ 在训练脚本中，需要指定模型存储的绝对路径。
+
+# 快速开始
-It's important to mention that we have not optimized the response speed of the application or the frontend logic. Our goal is to maintain simplicity, clarity, and ease of development, making it accessible for both researchers and students. If you have suggestions or want to enhance the application's performance, please feel free to contact us or contribute to the project.
+## 模型训练
+1. 训练脚本位置位于scripts目录，提供了train_pllava_single_npu.sh（单卡）、 train_pllava_multi_npu.sh（单机多卡）、train_pllava_npu_multi_node.sh（多机多卡）三个脚本。 需要根据真实值配置cann的set_env.sh路径、数据集路径、权重的路径。
-We've briefly introduce our work in section [PLLAVA](#%EF%B8%8F-pllava). For more details, feel free to read our paper. Check out section [Usage](#hammer-usage) to start using this repo. If you felt our works interesting, please star us, your support is all we want. If you find our work helpful, feel free to [cite](#page_facing_up-citation) us directly.
+2. 运行训练脚本，下面以单机单卡示例：
-## :fire: Updates
+ ```shell
+ bash scripts/train_pllava_single_npu.sh
+ ```
+ 训练完成后，权重文件保存在参数`--output_dir`路径下。
+## 结果展示
-- **2024/4/24**: Release:
- - We are releasing our code/models/datasets.
+**表 2** 训练结果展示：
-## 🏖️ PLLAVA
-
+| 芯片 | 卡数 | second per step | batch_size | AMP_Type | Torch_Version |
+|:-------------:|:---:|:---------------:|:----------:|:---:|:---:|
+| 竞品A | 8p | 0.84s | 1 | bf16 | 2.1 |
+| Atlas 800T A2 | 8p | 0.85s | 1 | bf16 | 2.1 |
-### Abstract
+## 模型推理
+训练脚本位置位于scripts目录下，提供了eval_single.sh脚本，其中的cann的set_env.sh路径、视频文件路径、模型文件路径、权重文件路径等，按照实际填写。
-Vision-language pre-training (VLP) has significantly elevated performance across a range of vision-language applications. Yet, the pre-training process for video-related tasks demands an exceptionally high degree of computational and data resources. This paper investigates a straightforward, highly efficient, and resource-light approach to adapting an existing image-language pre-training model for video data. Our preliminary experiments reveal that directly fine-tuning pre-trained image-language models with multiple frames on video datasets leads to performance saturation or even a drop in caption-related tasks. Besides, it is also vulnerable to prompts and tends to provide short descriptions. We conducted a deep analysis and observed that the performance saturation and the vulnerability might be related to the dominant patches that exist in some single video patches. We then propose a simple pooling strategy to smooth the feature distribution along the temporal dimension and thus reduce the dominant impacts from some extreme tokens. The new model is termed Pooling LLaVA, or PLLaVA in short. With the proposed pooling strategy, we achieve new state-of-the-art performance on all evaluated datasets. Notably, on the recent popular Video ChatGPT benchmark, PLLaVA achieves a score of 3.48 out of 5 on average of five evaluated dimensions, which is the new state-of-the-art score on the leaderboard and is 0.31 higher than the previous SOTA results from GPT4V (IG-VLM). On the latest multi-choice benchmark MVBench, PLLaVA achieves 58.1% accuracy on average across 20 sub-tasks, which is the new state-of-the-art result and is 14.5% higher than GPT4V (IG-VLM).
+ ```
+ bash scripts/eval_single.sh
+ ```
+脚本执行中，会让用户输入问题，再根据问题返回答案。
-
+# 公网地址说明
+代码涉及公网地址参考 [public_address_statement.md](public_address_statement.md)
-### SEARCHING FOR OPTIMAL POOLING STRATEGY
-There are two dimensions for the pooling strategy: the spatial dimension and the temporal dimension. We empirically found that reducing the spatial dimension with a larger temporal dimension could lead to better model performance, compared to reducing the temporal dimension directly.
-
+# 变更说明
+2024.08.09: 首次发布。
-
-### STATE-OF-THE-ART PERFORMANCE
-We compare the performance of PLLAVA with recent popular methods over both question-answer and captioning datasets. The results are shown below.
-
-
-
-## :hammer: Usage
-
-This section provides guidance on how to run, train, and evaluate our models.
-
-### Install
-First, you will need to set up the environment and download some pre-trained weights.
-
-This repo is built up using [transformers](https://github.com/huggingface/transformers) for model construction along with [accelerate](https://github.com/huggingface/accelerate) for distributed training. Follow the instructions to install the needed environment.
-
-1. Above all, the following environment set up is for python 3.10. If you choose to use conda for environment setup, we recommend creating the virtual environment with:
-```bash
-conda create -n pllava python=3.10
-```
-
-1. Firstly, install [pytorch](https://pytorch.org/) from the official website. The code runs on torch 2.2.1, cu118 or cu122. Select the version that suits your drive version.
-
-```
-torch 2.2.1+cu118
-torchaudio 2.2.1+cu118
-torchvision 0.17.1+cu118
-```
-
-If your driver version is higher than cu121, you could probably try installing with the following scripts:
-```bash
-pip install -r requirements.txt
-```
-
-Otherwise, you would need to install a torch for your server first, then install the other packages:
-```bash
-pip install -r requirements.torch.txt # decide your own requirements, (this is for cu11), or install torch directly following the official website.
-pip install -r requirements.no_torch.txt # install the following
-```
-
-1. Prepare the model.
-We prefer to have huggingface models explicitly downloaded to a MODELS directory. However, if you are familiar with huggingface-hub usage, feel free to organize the model yourself.
-```
-python python_scripts/hf.py
-```
-
-Here are some detailed information of the obtained models:
-
-
-| Model | Link | Initialized From |
-| ------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------------------------- |
-| pllava-7b | [](https://huggingface.co/ermu2001/pllava-7b) | [llava-hf/llava-v1.6-vicuna-7b-hf · Hugging Face](https://huggingface.co/llava-hf/llava-v1.6-vicuna-7b-hf) |
-| pllava-13b | [](https://huggingface.co/ermu2001/pllava-13b) | [llava-hf/llava-v1.6-vicuna-13b-hf · Hugging Face](https://huggingface.co/llava-hf/llava-v1.6-vicuna-13b-hf) |
-| pllava-34b | [](https://huggingface.co/ermu2001/pllava-34b) | [llava-hf/llava-v1.6-34b-hf · Hugging Face](https://huggingface.co/llava-hf/llava-v1.6-34b-hf) |
-
-The model directory should look like this, where you would only need the corresponding model's weights and directory.
-
-```
-$ tree MODELS
-MODELS
-|-- pllava-13b
-| |-- added_tokens.json
-| |-- config.json
-| |-- generation_config.json
-| |-- model-00001-of-00006.safetensors
-| |-- model-00002-of-00006.safetensors
-| |-- model-00003-of-00006.safetensors
-| |-- model-00004-of-00006.safetensors
-| |-- model-00005-of-00006.safetensors
-| |-- model-00006-of-00006.safetensors
-| |-- model.safetensors.index.json
-| |-- preprocessor_config.json
-| |-- processor_config.json
-| |-- special_tokens_map.json
-| |-- tokenizer.json
-| |-- tokenizer.model
-| `-- tokenizer_config.json
-|-- pllava-34b
-| |-- added_tokens.json
-| |-- config.json
-| |-- generation_config.json
-| |-- model-00001-of-00015.safetensors
-| |-- model-00002-of-00015.safetensors
-| |-- model-00003-of-00015.safetensors
-| |-- model-00004-of-00015.safetensors
-| |-- model-00005-of-00015.safetensors
-| |-- model-00006-of-00015.safetensors
-| |-- model-00007-of-00015.safetensors
-| |-- model-00008-of-00015.safetensors
-| |-- model-00009-of-00015.safetensors
-| |-- model-00010-of-00015.safetensors
-| |-- model-00011-of-00015.safetensors
-| |-- model-00012-of-00015.safetensors
-| |-- model-00013-of-00015.safetensors
-| |-- model-00014-of-00015.safetensors
-| |-- model-00015-of-00015.safetensors
-| |-- model.safetensors-deprecated
-| |-- model.safetensors.index.json
-| |-- preprocessor_config.json
-| |-- processor_config.json
-| |-- special_tokens_map.json
-| |-- tokenizer.json
-| |-- tokenizer.model
-| `-- tokenizer_config.json
-|-- pllava-7b
- |-- added_tokens.json
- |-- config.json
- |-- generation_config.json
- |-- model-00001-of-00003.safetensors
- |-- model-00002-of-00003.safetensors
- |-- model-00003-of-00003.safetensors
- |-- model.safetensors.index.json
- |-- preprocessor_config.json
- |-- processor_config.json
- |-- special_tokens_map.json
- |-- tokenizer.json
- |-- tokenizer.model
- `-- tokenizer_config.json
-```
-
-With the above steps, you should be able to proceed on with the following usages.
-
-### Run Application
-
-To run our models, make sure you have downloaded a model pretrained weights from the huggingface spaces. Then, run the following scripts with the corresponding path input. Since we are only training with lora and the projector, the model to be run are determined with:
-
-- **model_dir**: model directory, one with config.json as compatible with transformers. This refers to the base model's directory, for example "llava-hf/llava-v1.6-vicuna-7b-hf"/"ermu2001/pllava-7b"/"MODELS/pllava-7b". (default to: MODELS/plave-7b)
-- **weights_dir**: your weights directory. could be the same as model_dir, but if you have a weights directory for the lora weights, you should set this weights_dir to that directory to load the lora weights. This directory should be local. Also, it would need to contain a config.json file within. (default to: ${model_dir}).
-
-```bash
-model_dir="model directory"
-weights_dir="weights directory"
-bash scripts/demo.sh ${model_dir} ${weights_dir}
-```
-
-Now check out the application demo and try play with PLLAVA!
-
-### Train
-
-Follow the following steps to reproduce our results or train your own variant:
-
-#### 1. Data Preparation
-
-To train our model from a starting Image-aligned Vision LLM, you would need to download the data first. Our data set up is mainly based on the original Videochat2's training data. Check out [Instruction Data](./DATA.md) to prepare the instruction training data. Ideally, setting up a root data directory and alter the code [here](./tasks/train/instruction_data.py#L6) would accomodate the data for training most smoothly.
-
-#### 2. Start Training
-
-Now you're only a few step away from starting the training. Follow the instructions:
-
-##### Setup Accelerator
-
-Customize a accelerate training config. For example, a simple config using multiple gpus with no distribution strategy (only torch DDP) would look like:
-
-```yaml
-compute_environment: LOCAL_MACHINE
-debug: false
-distributed_type: MULTI_GPU
-downcast_bf16: 'no'
-gpu_ids: all
-machine_rank: 0
-main_training_function: main
-mixed_precision: bf16
-num_machines: 1
-num_processes: 8
-rdzv_backend: static
-same_network: true
-tpu_env: []
-tpu_use_cluster: false
-tpu_use_sudo: false
-use_cpu: false
-```
-
-Check out out the [Accelerate](https://huggingface.co/docs/accelerate/index) documents for more details.
-
-##### Overwatch the training configuration
-
-Next, you should go over a basic training configuration of the training process in [here](tasks/train/config_pllava_nframe.py). Then passing this file as the first arg to the [training script](tasks/train/train_pllava_nframe_accel.py) would utilize every arguments in the file. You can customize some of the hyper parameters for your own training process by passing them in the format of "key" "value" pair in the following arguments. A example training scripts could be find [here](scripts/train_pllava.sh).
-
-We recommand customize a [configuration](tasks/train/config_pllava_nframe.py) to set up a customized training!
-
-With the above steps, you would be able to start the training process. The output would be well organized in the output directory, each a qualified model directory to pass in to demo as weights_dir, since we are only saveing the lora weights and projector weights to avoide redundancy.
-
-### Evaluation
-
-This section mainly introduce how to reproduce the evaluation or evaluate your own model.
-
-#### Set up Evaluation Data
-
-Make sure you set up the "DATAS" directory as in [DATA.md](DATA.md), then you would be able to run the inference with fortune! The evaluation data directory of DATAS would look like:
-
-```
-DATAS/:
-DATAS/VideoQA:
-DATAS/VideoQA/TGIF_QA:
- test_a.json
- test_q.json
-DATAS/VideoQA/TGIF_QA/videos:
- tumblr_m4387mGrlc1r6m5e8o1_250.gif
- ...
-DATAS/VideoQA/TGIF_QA/videos_mp4:
- tumblr_m4387mGrlc1r6m5e8o1_250.mp4
- ...
-DATAS/VideoQA/TGIF_QA/video_gif:
- tumblr_m4387mGrlc1r6m5e8o1_250.gif
- ...
-DATAS/VideoQA/MSVD_Zero_Shot_QA:
- test_a.json
- test_q.json
-DATAS/VideoQA/MSVD_Zero_Shot_QA/videos:
- -4wsuPCjDBc_5_15.avi
-DATAS/VideoQA/MSVD_Zero_Shot_QA/msvd_qa:
-DATAS/VideoQA/ActivityNet:
- test_a.json
- test_q.json
-DATAS/VideoQA/ActivityNet/all_test:
- v_--tFD65KaK4.mp4
- ...
-DATAS/VideoQA/MSRVTT_Zero_Shot_QA:
- test_a.json
- test_q.json
-DATAS/VideoQA/MSRVTT_Zero_Shot_QA/videos:
-DATAS/VideoQA/MSRVTT_Zero_Shot_QA/videos/all:
- video0.mp4
- ...
-
-DATAS/MVBench:
- ...
-
-DATAS/Recaption/Inter4K:
- annotations.json
-DATAS/Recaption/Inter4K/60fps:
-DATAS/Recaption/Inter4K/60fps/UHD:
- 1.mp4
- ...
-
-```
-
-#### Start Evaluate
-
-Once you have construted the evaluation data, you can start the evaluation as in [here](scripts/eval.sh). This script is for evaluating 7B/13B models. As pllava-34b model uses a slightly different prompting, it is evaluated with this [script](scripts/eval_yiprompt.sh).
-
-```
-bash scripts/eval.sh
-```
-
-Same as running the demo, you would need to determine the model_dir and weights_dir to evaluate the model. Feel free to comment out some commands and produce partial evaluation.
-
-#### Overwatch the Results
-
-The evaluation results would be shown to you with our results gallery demo:
-
-```bash
-bash scripts/gallery.sh
-```
-
-Feel free to use the compare version to compare differnt models' results or use the single gallery version to check out one model's results. They are basically the same. Check out this [script](scripts/gallery.sh) for more details
-
-#### For Captioning and Recaptioning
-Follow instructions at [DATA.md](DATA.md#extending-reacptioning) and you can extend the recaptioning data with a few steps.
-
-Feel free to point out high quality dataset of videos, we would proceed on doing captioning on those datasets.
-
-
-# :page_facing_up: Citation
-
-If you find this project useful in your research, please consider cite:
-
-```BibTeX
-@misc{xu2024pllava,
- title={PLLaVA : Parameter-free LLaVA Extension from Images to Videos for Video Dense Captioning},
- author={Lin Xu and Yilin Zhao and Daquan Zhou and Zhijie Lin and See Kiong Ng and Jiashi Feng},
- year={2024},
- eprint={2404.16994},
- archivePrefix={arXiv},
- primaryClass={cs.CV}
-}
-```
-
-# :dizzy: Acknowledgement
-
-This code base is mainly built upon [Videochat2](https://github.com/OpenGVLab/Ask-Anything/tree/main/video_chat2). SALUTE.
-
-We would also like to recognize and commend the following open source projects, thank you for your great contribution to the open source community:
-
-- [LLaVA](https://github.com/haotian-liu/LLaVA): Fantastic Open Source Image LLM Model.
-- [VideoChatGPT](https://github.com/mbzuai-oryx/Video-ChatGPT/tree/main): Great Evaluation Benchmarking Framework.
-- [VideoLlava](https://github.com/PKU-YuanGroup/Video-LLaVA/tree/main/videollava)：Video LLM repo with helpful resources.
+# FAQ
+无
diff --git a/PyTorch/built-in/mlm/PLLaVA/README_en.md b/PyTorch/built-in/mlm/PLLaVA/README_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..b07bc8e8c546c10c547edffc0bcdc6bd9da6f13d
--- /dev/null
+++ b/PyTorch/built-in/mlm/PLLaVA/README_en.md
@@ -0,0 +1,372 @@
+
+
+
+
+[Lin Xu](https://scholar.google.com/citations?user=_Gu69coAAAAJ), [Yilin Zhao](https://ermu2001.github.io/me.io/), [Daquan Zhou](https://scholar.google.com/citations?user=DdCAbWwAAAAJ), [Zhijie Lin](https://scholar.google.com/citations?user=xXMj6_EAAAAJ), [See-Kiong Ng](https://scholar.google.com/citations?user=_wsommYAAAAJ), [Jiashi Feng](https://scholar.google.com.sg/citations?user=Q8iay0gAAAAJ&hl=en)
+
+
+
+
+
+
+
+
+
+## Overview
+
+Welcome to PLLAVA!
+
+The primary purpose of this repository is to support research and the development of prototype models. It is designed to facilitate ease of experimentation and enable a clear overview of results. Please note that this repo is currently undergoing development and reconstruction.
+
+It's important to mention that we have not optimized the response speed of the application or the frontend logic. Our goal is to maintain simplicity, clarity, and ease of development, making it accessible for both researchers and students. If you have suggestions or want to enhance the application's performance, please feel free to contact us or contribute to the project.
+
+
+We've briefly introduce our work in section [PLLAVA](#%EF%B8%8F-pllava). For more details, feel free to read our paper. Check out section [Usage](#hammer-usage) to start using this repo. If you felt our works interesting, please star us, your support is all we want. If you find our work helpful, feel free to [cite](#page_facing_up-citation) us directly.
+
+## :fire: Updates
+
+- **2024/4/24**: Release:
+ - We are releasing our code/models/datasets.
+
+## 🏖️ PLLAVA
+
+
+
+### Abstract
+
+Vision-language pre-training (VLP) has significantly elevated performance across a range of vision-language applications. Yet, the pre-training process for video-related tasks demands an exceptionally high degree of computational and data resources. This paper investigates a straightforward, highly efficient, and resource-light approach to adapting an existing image-language pre-training model for video data. Our preliminary experiments reveal that directly fine-tuning pre-trained image-language models with multiple frames on video datasets leads to performance saturation or even a drop in caption-related tasks. Besides, it is also vulnerable to prompts and tends to provide short descriptions. We conducted a deep analysis and observed that the performance saturation and the vulnerability might be related to the dominant patches that exist in some single video patches. We then propose a simple pooling strategy to smooth the feature distribution along the temporal dimension and thus reduce the dominant impacts from some extreme tokens. The new model is termed Pooling LLaVA, or PLLaVA in short. With the proposed pooling strategy, we achieve new state-of-the-art performance on all evaluated datasets. Notably, on the recent popular Video ChatGPT benchmark, PLLaVA achieves a score of 3.48 out of 5 on average of five evaluated dimensions, which is the new state-of-the-art score on the leaderboard and is 0.31 higher than the previous SOTA results from GPT4V (IG-VLM). On the latest multi-choice benchmark MVBench, PLLaVA achieves 58.1% accuracy on average across 20 sub-tasks, which is the new state-of-the-art result and is 14.5% higher than GPT4V (IG-VLM).
+
+
-
-
-
-