xaxm007 / video-captioning-transformer

For transformer understanding

Video-Captioning-Transformer

For transformer understanding (This is just a note for me to implement the original project).

Video Captioning Transformer Project

This project aims to generate captions for videos using a Transformer model. The project integrates multiple repositories, datasets, and pre-trained models to create a comprehensive video captioning solution. Below is a detailed guide on setting up and using the project.

Table of Contents

1. Repositories
2. Datasets
3. Pre-trained Models
4. Dependencies
5. Setup Instructions
6. Usage
7. Notes

Repositories

Main Repositories

• Video-Captioning-Transformer

  • Repository: Video-Captioning-Transformer
  • Description: Transformer model for video captioning.

• Video-Features

  • Repository: Video-Features
  • Description: Repository for extracting video features.

Datasets

• Dataloader

  • Repository: MSVD Dataloader
  • Description: Dataloader for MSVD dataset.

• Baidu Dataset

  • Link: Baidu MSRVTT and MSVD Dataset
  • Password: aupi
  • Description: MSRVTT and MSVD datasets available for download.

Pre-trained Models

• CLIP4Clip Model

  • Model File: clip4clip_msrvtt.pth
  • Paper: CLIP4Clip Paper
  • Repository: CLIP4Clip Repo

• I3D Model

  • Repository: ID3 Model
  • Description: Pre-trained I3D model for extracting video features.

Dependencies

• mmcv
  • Installation Guide: mmcv Installation
  • Note: Follow the instructions carefully to avoid errors.

Setup Instructions

1. Create Conda Environment

conda create -n video_captioning python=3.8
conda activate video_captioning

To ensure the project runs smoothly, follow these additional steps:

Setting Up Data Loaders

1. Navigate to the Video-Captioning-Transformer repository.

2. Configure the data loader to use the MSVD dataset:

   • Edit the configuration file to set the path to your MSVD dataset.
   • Example:

     dataset:
       name: MSVD
       path: /path/to/your/MSVD/dataset

3. Configure the data loader to use the MSRVTT dataset:

   • Edit the configuration file to set the path to your MSRVTT dataset.
   • Example:

     dataset:
       name: MSRVTT
       path: /path/to/your/MSRVTT/dataset

Training the Model

1. Ensure you are in the Video-Captioning-Transformer directory.

2. Run the training script with the appropriate configuration:

   python train.py --config configs/train_config.yaml

Additional Transformer Repositories

In addition to the main repositories, the project also integrates the following repositories for enhanced transformer capabilities:

• BMT (Bidirectional Multimodal Transformer)

  • Repository: BMT
  • Description: Bidirectional Multimodal Transformer for multimodal tasks.

• MDVC (Modality Distillation with Visual Concept)

  • Repository: MDVC
  • Description: Repository for modality distillation with visual concepts.

These repositories offer additional transformer architectures and functionalities, further enhancing the capabilities of the video captioning transformer model.