Languagebind Video V1.5 FT

🚀 [LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment]

LanguageBind is a language - centric multimodal pretraining approach. It extends video - language pretraining to N - modality through language - based semantic alignment, offering high - performance multimodal solutions with a large - scale dataset.

📄 License

This project is licensed under the MIT license.

📝 Project Logo

📖 Project Title

【ICLR 2024 🔥】LanguageBind: Extending Video-Language Pretraining to N-modality by Language-based Semantic Alignment

💖 Invitation

If you like our project, please give us a star ⭐ on GitHub for latest update.

📰 News

• [2024.01.27] 👀👀👀 Our [MoE - LLaVA](https://github.com/PKU - YuanGroup/MoE - LLaVA) is released! A sparse model with 3B parameters outperformed the dense model with 7B parameters.
• [2024.01.16] 🔥🔥🔥 Our LanguageBind has been accepted at ICLR 2024! We earn the score of 6(3)8(6)6(6)6(6) here.
• [2023.12.15] 💪💪💪 We expand the 💥💥💥 VIDAL dataset and now have 10M video - text data. We launch LanguageBind_Video 1.5, checking our model zoo.
• [2023.12.10] We expand the 💥💥💥 VIDAL dataset and now have 10M depth and 10M thermal data. We are in the process of uploading thermal and depth data on [Hugging Face](https://huggingface.co/datasets/LanguageBind/VIDAL - Depth - Thermal) and expect the whole process to last 1 - 2 months.
• [2023.11.27] 🔥🔥🔥 We have updated our paper with emergency zero - shot results., checking our ✨ [results](#emergency - results).
• [2023.11.26] 💥💥💥 We have open - sourced all textual sources and corresponding YouTube IDs here.
• [2023.11.26] 📣📣📣 We have open - sourced fully fine - tuned Video & Audio, achieving improved performance once again, checking our model zoo.
• [2023.11.22] We are about to release a fully fine - tuned version, and the HUGE version is currently undergoing training.
• [2023.11.21] 💥 We are releasing sample data in DATASETS.md so that individuals who are interested can further modify the code to train it on their own data.
• [2023.11.20] 🚀🚀🚀 [Video - LLaVA](https://github.com/PKU - YuanGroup/Video - LLaVA) builds a large visual - language model to achieve 🎉SOTA performances based on LanguageBind encoders.
• [2023.10.23] 🎶 LanguageBind - Audio achieves 🎉🎉🎉state - of - the - art (SOTA) performance on 5 datasets, checking our ✨ [results](#multiple - modalities)!
• [2023.10.14] 😱 Released a stronger LanguageBind - Video, checking our ✨ [results](#video - language)! The video checkpoint have updated on Huggingface Model Hub!
• [2023.10.10] We provide sample data, which can be found in assets, and [emergency zero - shot usage](#emergency - zero - shot) is described.
• [2023.10.07] The checkpoints are available on 🤗 Huggingface Model.
• [2023.10.04] Code and demo are available now! Welcome to watch 👀 this repository for the latest updates.

😮 Highlights

💡 High performance, but NO intermediate modality required

LanguageBind is a language - centric multimodal pretraining approach, taking the language as the bind across different modalities because the language modality is well - explored and contains rich semantics.

• The following first figure shows the architecture of LanguageBind. LanguageBind can be easily extended to segmentation, detection tasks, and potentially to unlimited modalities.

⚡️ A multimodal, fully aligned and voluminous dataset

We propose VIDAL - 10M, 10 Million data with Video, Infrared, Depth, Audio and their corresponding Language, which greatly expands the data beyond visual modalities.

• The second figure shows our proposed VIDAL - 10M dataset, which includes five modalities: video, infrared, depth, audio, and language.

🔥 Multi - view enhanced description for training

We make multi - view enhancements to language. We produce multi - view description that combines meta - data, spatial, and temporal to greatly enhance the semantic information of the language. In addition we further enhance the language with ChatGPT to create a good semantic space for each modality aligned language.

🤗 Demo

🔧 Local demo

Highly recommend trying out our web demo, which incorporates all features currently supported by LanguageBind.

python gradio_app.py

🌐 Online demo

We provide the online demo in Huggingface Spaces. In this demo, you can calculate the similarity of modalities to language, such as audio - to - language, video - to - language, and depth - to - image.

🛠️ Requirements and Installation

• Python >= 3.8
• Pytorch >= 1.13.1
• CUDA Version >= 11.6
• Install required packages:

git clone https://github.com/PKU-YuanGroup/LanguageBind
cd LanguageBind
pip install torch==1.13.1+cu116 torchvision==0.14.1+cu116 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116
pip install -r requirements.txt

🐳 Model Zoo

The names in the table represent different encoder models. For example, LanguageBind/LanguageBind_Video_FT represents the fully fine - tuned version, while LanguageBind/LanguageBind_Video represents the LoRA - tuned version.

You can freely replace them in the recommended API usage. We recommend using the fully fine - tuned version, as it offers stronger performance.

🔍 Encoder Model Table

📊 Performance Table

💻 Usage Examples

📌 Basic Usage

We open source all modalities preprocessing code. If you want to load the model (e.g. LanguageBind/LanguageBind_Thermal) from the model hub on Huggingface or on local, you can use the following code snippets!

import torch
from languagebind import LanguageBind, to_device, transform_dict, LanguageBindImageTokenizer

if __name__ == '__main__':
    device = 'cuda:0'
    device = torch.device(device)
    clip_type = {
        'video': 'LanguageBind_Video_FT',  # also LanguageBind_Video
        'audio': 'LanguageBind_Audio_FT',  # also LanguageBind_Audio
        'thermal': 'LanguageBind_Thermal',
        'image': 'LanguageBind_Image',
        'depth': 'LanguageBind_Depth',
    }

    model = LanguageBind(clip_type=clip_type, cache_dir='./cache_dir')
    model = model.to(device)
    model.eval()
    pretrained_ckpt = f'lb203/LanguageBind_Image'
    tokenizer = LanguageBindImageTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir/tokenizer_cache_dir')
    modality_transform = {c: transform_dict[c](model.modality_config[c]) for c in clip_type.keys()}

    image = ['assets/image/0.jpg', 'assets/image/1.jpg']
    audio = ['assets/audio/0.wav', 'assets/audio/1.wav']
    video = ['assets/video/0.mp4', 'assets/video/1.mp4']
    depth = ['assets/depth/0.png', 'assets/depth/1.png']
    thermal = ['assets/thermal/0.jpg', 'assets/thermal/1.jpg']
    language = ["Training a parakeet to climb up a ladder.", 'A lion climbing a tree to catch a monkey.']

    inputs = {
        'image': to_device(modality_transform['image'](image), device),
        'video': to_device(modality_transform['video'](video), device),
        'audio': to_device(modality_transform['audio'](audio), device),
        'depth': to_device(modality_transform['depth'](depth), device),
        'thermal': to_device(modality_transform['thermal'](thermal), device),
    }
    inputs['language'] = to_device(tokenizer(language, max_length=77, padding='max_length',
                                             truncation=True, return_tensors='pt'), device)

    with torch.no_grad():
        embeddings = model(inputs)

    print("Video x Text: \n",
          torch.softmax(embeddings['video'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
    print("Image x Text: \n",
          torch.softmax(embeddings['image'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
    print("Depth x Text: \n",
          torch.softmax(embeddings['depth'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
    print("Audio x Text: \n",
          torch.softmax(embeddings['audio'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())
    print("Thermal x Text: \n",
          torch.softmax(embeddings['thermal'] @ embeddings['language'].T, dim=-1).detach().cpu().numpy())

The output result is:

Video x Text: 
 [[9.9989331e-01 1.0667283e-04]
 [1.3255903e-03 9.9867439e-01]]
Image x Text: 
 [[9.9990666e-01 9.3292067e-05]
 [4.6132666e-08 1.0000000e+00]]
Depth x Text: 
 [[0.9954276  0.00457235]
 [0.12042473 0.8795753 ]]
Audio x Text: 
 [[0.97634876 0.02365119]
 [0.02917843 0.97082156]]
Thermal x Text: 
 [[0.9482511  0.0517489 ]
 [0.48746133 0.5125386 ]]

🌟 Advanced Usage

🚀 Emergency zero - shot

Since languagebind binds each modality together, we also found the emergency zero - shot. It's very simple to use.

print("Video x Audio: \n", torch.softmax(embeddings['video'] @ embeddings['audio'].T, dim=-1).detach().cpu().numpy())
print("Image x Depth: \n", torch.softmax(embeddings['image'] @ embeddings['depth'].T, dim=-1).detach().cpu().numpy())
print("Image x Thermal: \n", torch.softmax(embeddings['image'] @ embeddings['thermal'].T, dim=-1).detach().cpu().numpy())

The output is:

Video x Audio: 
 [[1.0000000e+00 0.0000000e+00]
 [3.1150486e-32 1.0000000e+00]]
Image x Depth: 
 [[1. 0.]
 [0. 1.]]
Image x Thermal: 
 [[1. 0.]
 [0. 1.]]

📚 Different branches for X - Language task

Additionally, LanguageBind can be disassembled into different branches to handle different tasks. Note that we do not train Image, which just initialize from OpenCLIP.

🌡️ Thermal

import torch
from languagebind import LanguageBindThermal, LanguageBindThermalTokenizer, LanguageBindThermalProcessor

pretrained_ckpt = 'LanguageBind/LanguageBind_Thermal'
model = LanguageBindThermal.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
tokenizer = LanguageBindThermalTokenizer.from_pretrained(pretrained_ckpt, cache_dir='./cache_dir')
thermal_process = LanguageBindThermalProcessor(model.config, tokenizer)

model.eval()
data = thermal_process([r"your/thermal.jpg"], ['your text'], return_tensors='pt')
with torch.no_grad():
    out = model(**data)

print(out.text_embeds @ out.image_embeds.T)

📏 Depth

# The original README didn't provide the complete code for depth, so it remains incomplete here.
import torch
from languagebind import ...