|
--- |
|
license: mit |
|
--- |
|
|
|
## This is the benchmark dataset for ["A Benchmark for Multi-modal Foundation Models on Low-level Vision: from Single Images to Pairs"](https://arxiv.org/abs/2402.07116) |
|
|
|
# The structure of the jsonl files is as follows: |
|
|
|
1. q-bench2-a1-dev.jsonl (**with** *img_path*, *question*, *answer_candidates*, *correct_answer*) |
|
2. q-bench2-a1-test.jsonl (**with** *img_path*, *question*, *answer_candidates*, **without** *correct_answer*) |
|
3. q-bench2-a2.jsonl (**with** *img_path*, *empty response*) |
|
|
|
# The img_path is organized as *prefix* + *img1* + \_cat\_ + *img2* + *.jpg* |
|
|
|
For example, if the img_path is "llvisionqa_compare_dev\\\\00079.jpg_cat_09769.jpg.jpg", then the prefix is "llvisionqa_compare_dev", img1 is "00079.jpg", img2 is "09769.jpg". |
|
|
|
You can use the function to get the image paths: |
|
|
|
``` |
|
def get_img_names(img_path, prefix = "path_to_all_single_images"): |
|
img_paths = img_path.split('\\')[1][:-4].split("_cat_") |
|
img1_name = os.path.join(prefix,img_paths[0]) |
|
img2_name = os.path.join(prefix,img_paths[1]) |
|
return img1_name,img2_name |
|
``` |
|
# The image file structure is: |
|
|
|
1. all_single_images: all of the single images used, [Baiduyunpan download link](https://pan.baidu.com/s/1fr8A0sqDxWuznsZEC_bHrQ?pwd=tzp5) |
|
2. llvisionqa_compare_dev: the concatenated images for the dev subset of the perception-compare task |
|
3. llvisionqa_compare_test: the concatenated images for the test subset of the perception-compare task |
|
4. lldescribe_compare: the concatenated images for the description-compare task |
|
|
|
# Submission for test your own MLLM on q-bench2 |
|
|
|
1. Perception-compare task (a1): organize your jsonl file "q-bench2-a1-test_(YOUR_MLLM_NAME).jsonl" as the structure of the provided "q-bench2-a1-dev.jsonl" |
|
2. Description-compare task (a2): simply complete the empty "response" of "q-bench2-a2.jsonl" file and rename into "q-bench2-a2_(YOUR_MLLM_NAME).jsonl" |
|
|
|
Please contact any of the first authors to get the results of your MLLM with the submission files. |
|
|
|
Zicheng Zhang, zzc1998@sjtu.edu.cn |
|
Haoning Wu, haoning001@e.ntu.edu.sg |
|
|
|
|
