Update README.md

README.md

@@ -3,21 +3,27 @@ dataset_info:
   features:
   - name: image
     dtype: image
-  - name: label
-    dtype:
-      class_label:
-        names:
-          '0': test
-          '1': train
+  - name: question
+    dtype: string
+  - name: options
+    sequence: string
+  - name: answer
+    dtype: string
+  - name: category
+    dtype: string
+  - name: id
+    dtype: int64
+  - name: source
+    dtype: string
   splits:
   - name: train
-    num_bytes: 48882
+    num_bytes: 145603
     num_examples: 4
   - name: test
-    num_bytes: 24413829
-    num_examples: 1000
-  download_size: 24468235
-  dataset_size: 24462711
+    num_bytes: 15782648
+    num_examples: 435
+  download_size: 14338600
+  dataset_size: 15928251
 configs:
 - config_name: default
   data_files:
@@ -28,6 +34,7 @@ configs:
 task_categories:
 - image-to-text
 - visual-question-answering
+- question-answering
 language:
 - en
 size_categories:
@@ -35,8 +42,10 @@ size_categories:
 ---
 # IllusionVQA: Optical Illusion Dataset
 
-Paper Link: <br>
-Github Link: <be>
+[Project Page](https://illusionvqa.github.io/) | 
+[Paper](https://arxiv.org/abs/2403.15952) |
+[Github](https://github.com/csebuetnlp/IllusionVQA/)
+
 ## TL;DR
 IllusionVQA is a dataset of optical illusions and hard-to-interpret scenes designed to test the capability of Vision Language Models in comprehension and soft localization tasks. GPT4V achieved 62.99% accuracy on comprehension and 49.7% on localization, while humans achieved 91.03% and 100% respectively.
 
@@ -69,17 +78,17 @@ def construct_mcq(options, correct_option):
 def add_row(content, data, i, with_answer=False):  
     mcq, correct_option_letter = construct_mcq(data["options"], data["answer"])
     content.append({ "type": "text",
-            "text": "Image "+str(i)+": "+data["question"]+"\n"+mcq })
+            "text": "Image " + str(i) + ": " + data["question"] + "\n" + mcq })
     content.append({ "type": "image_url",
             "image_url": {"url": f"data:image/jpeg;base64,{encode_image(data["image"])}",
                 "detail": "low"}})
     if with_answer:
-        content.append({"type": "text", "text": "Answer {}: ".format(i)+correct_option_letter})
+        content.append({"type": "text", "text": "Answer {}: ".format(i) + correct_option_letter})
     else:
         content.append({"type": "text", "text": "Answer {}: ".format(i), })
     return content
 
-dataset = load_dataset("csebuetnlp/illusionVQA-Soft-Localization")
+dataset = load_dataset("csebuetnlp/illusionVQA-Comprehension")
 client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
 
 content = [{
@@ -87,13 +96,12 @@ content = [{
         "text": "You'll be given an image, an instruction and some choices. You have to select the correct one. Do not explain your reasoning. Answer with the option's letter from the given choices directly. Here are a few examples:",
     }]
 
-### Add the few examples
-i = 1
-for data in dataset["train"]:
+### Add a few examples
+for i, data in enumerate(dataset["train"], 1):
     content = add_row(content, data, i, with_answer=True)
-    i += 1
 
 content.append({"type": "text","text": "Now you try it!",})
+
 next_idx = i
 
 ### Add the test data
@@ -108,14 +116,25 @@ response = client.chat.completions.create(
 )
 gpt4_answer = response.choices[0].message.content
 print(gpt4_answer)
+
 ```
 
 ## License
-This dataset is made available for non-commercial research purposes only, including for evaluation of model performance. The dataset may not be used for training models. The dataset contains images collected from the internet. While permission has been obtained from some of the images' creators, permission has not yet been received from all creators. If you believe any image in this dataset is used without proper permission and you are the copyright holder, please email [email protected] to request the removal of the image from the dataset.
+This dataset is made available for non-commercial research purposes only under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (CC BY-NC-SA 4.0)](https://creativecommons.org/licenses/by-nc-sa/4.0/). The dataset may not be used for training models. The dataset contains images collected from the internet. While permission has been obtained from some of the images' creators, permission has not yet been received from all creators. If you believe any image in this dataset is used without proper permission and you are the copyright holder, please email [email protected] to request the removal of the image from the dataset.
 
 The dataset creator makes no representations or warranties regarding the copyright status of the images in the dataset. The dataset creator shall not be held liable for any unauthorized use of copyrighted material that may be contained in the dataset.
 
 You agree to the terms and conditions specified in this license by downloading or using this dataset. If you do not agree with these terms, do not download or use the dataset.
 
+<a rel="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png" /></a>
+
 
 ### Citation
+```
+@article{shahgir2024illusionvqa,
+      title={IllusionVQA: A Challenging Optical Illusion Dataset for Vision Language Models}, 
+      author={Haz Sameen Shahgir and Khondker Salman Sayeed and Abhik Bhattacharjee and Wasi Uddin Ahmad and Yue Dong and Rifat Shahriyar},
+      year={2024},
+      url={https://arxiv.org/abs/2403.15952},
+}
+```