Update README.md

README.md

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 license: mit
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+## 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 dataset 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*)
+4. q-bench-compare.zip (image files)
+
+# 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 q-bench-compare.zip structure is:
+
+1. all_single_images: all of the single images used
+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
+