Create README.md
Browse files
README.md
ADDED
@@ -0,0 +1,46 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
---
|
2 |
+
tags:
|
3 |
+
- vision-language model
|
4 |
+
- llama
|
5 |
+
- video understanding
|
6 |
+
---
|
7 |
+
|
8 |
+
|
9 |
+
# LLaMA-VID Model Card
|
10 |
+
<a href='https://llama-vid.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a>
|
11 |
+
<a href='https://arxiv.org/abs/2311.17043'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a>
|
12 |
+
|
13 |
+
## Model details
|
14 |
+
LLaMA-VID empowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token.
|
15 |
+
|
16 |
+
|
17 |
+
**Model type:**
|
18 |
+
LLaMA-VID is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
|
19 |
+
LLaMA-VID empowers existing frameworks to support hour-long videos and pushes their upper limit with an extra context token. We build this repo based on LLaVA.
|
20 |
+
|
21 |
+
|
22 |
+
**Model date:**
|
23 |
+
llama-vid-13b-full-224-video-fps-1 was trained on 11/2023.
|
24 |
+
|
25 |
+
## License
|
26 |
+
Llama 2 is licensed under the LLAMA 2 Community License,
|
27 |
+
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
|
28 |
+
|
29 |
+
**Where to send questions or comments about the model:**
|
30 |
+
https://github.com/dvlab-research/LLaMA-VID/issues
|
31 |
+
|
32 |
+
## Intended use
|
33 |
+
**Primary intended uses:**
|
34 |
+
The primary use of LLaMA-VID is research on large multimodal models and chatbots.
|
35 |
+
|
36 |
+
**Primary intended users:**
|
37 |
+
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
|
38 |
+
|
39 |
+
## Training data
|
40 |
+
This model is trained based on image data from LLaVA-1.5 dataset, and video data from WebVid and ActivityNet datasets, including
|
41 |
+
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
|
42 |
+
- 158K GPT-generated multimodal instruction-following data.
|
43 |
+
- 450K academic-task-oriented VQA data mixture.
|
44 |
+
- 40K ShareGPT data.
|
45 |
+
- 232K video-caption pairs sampled from the WebVid 2.5M dataset.
|
46 |
+
- 98K videos from ActivityNet with QA pairs from Video-ChatGPT.
|