|
--- |
|
license: mit |
|
--- |
|
|
|
# Visual Haystacks Dataset Card |
|
|
|
## Dataset details |
|
1. Dataset type: Visual Haystacks (VHs) is a benchmark dataset specifically designed to evaluate the Large Multimodal Model's (LMM's) capability to handle long-context visual information. It can also be viewed as the first vision-centric Needle-In-A-Haystack (NIAH) benchmark dataset. Please also download COCO-2017's training set validation set. |
|
|
|
2. Data Preparation and Benchmarking |
|
- Download the VQA questions: |
|
``` |
|
huggingface-cli download --repo-type dataset tsunghanwu/visual_haystacks --local-dir dataset/VHs_qa |
|
``` |
|
- Download the COCO 2017 dataset and organize it as follows, with the default root directory ./dataset/coco: |
|
``` |
|
dataset/ |
|
├── coco |
|
│ ├── annotations |
|
│ ├── test2017 |
|
│ └── val2017 |
|
└── VHs_qa |
|
├── single_needle |
|
│ ├── VHs_large |
|
│ └── VHs_small |
|
└── multi_needle |
|
``` |
|
- Follow the instructions in https://github.com/visual-haystacks/vhs_benchmark to run the evaluation. |
|
|
|
3. We utilized the full dataset from `single_needle/VHs_large` and `multi_needle`, which includes 1,000 test cases, to conduct all experiments depicted in Figures 2 and 3 with fewer than 100 images, and a third of this dataset for experiments with more than 100 input images. Additionally, the `single_needle/VHs_small` dataset, comprising 100 test cases, was employed specifically for the experiments on positional biases (Figure 4). |
|
|
|
4. Please check out our [project page](https://visual-haystacks.github.io) for more information. You can also send questions or comments about the model to [our github repo](https://github.com/visual-haystacks/vhs_benchmark/issues). |
|
|
|
5. This is the updated VHs dataset, enhanced for greater diversity and balance. The original dataset can be found at [tsunghanwu/visual_haystacks_v0](https://huggingface.co/datasets/tsunghanwu/visual_haystacks_v0). |
|
|
|
## Intended use |
|
Primary intended uses: The primary use of VHs is research on large multimodal models and chatbots. |
|
|
|
Primary intended users: The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence. |
|
|
