Upload 2 files

CAPMIT1003.py

@@ -8,11 +8,11 @@ import pandas as pd
 import sqlite3
 import datasets
 
-_CITATION = """```@article{zanca2023contrastive,
+_CITATION = """@article{zanca2023contrastive,
   title={Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors},
   author={Zanca, Dario and Zugarini, Andrea and Dietz, Simon and Altstidl, Thomas R and Ndjeuha, Mark A Turban and Schwinn, Leo and Eskofier, Bjoern},
   journal={arXiv preprint arXiv:2305.12380},
-  year={2023}```
+  year={2023}
 }"""
 
 _DESCRIPTION = """CapMIT1003 is a dataset of captions and click-contingent image explorations collected during captioning tasks. 
@@ -90,22 +90,6 @@ class CapMIT1003DB:
         return pd.read_sql_query('SELECT x, y, click_time AS time FROM clicks WHERE obs_uid = ?', self.cnx,
                                  params=[obs_uid])
 
-    @staticmethod
-    def download_images(quiet=False):
-        """ Download stimuli images for MIT1003.
-
-        Parameters
-        ----------
-        quiet: bool
-            Flag that suppresses command-line outputs.
-        """
-        if not os.path.exists('mit1003'):
-            if not os.path.exists('mit1003.zip'):
-                print('Downloading MIT1003 Stimuli') if not quiet else None
-                urlretrieve(MIT1003_URL, 'mit1003.zip')
-            print('Extracting MIT1003 Stimuli') if not quiet else None
-            unpack_archive('mit1003.zip', 'mit1003')
-
 
 class CapMIT1003(datasets.GeneratorBasedBuilder):
     _URLS = [MIT1003_URL]
@@ -119,7 +103,6 @@ class CapMIT1003(datasets.GeneratorBasedBuilder):
                     "usr_uid": datasets.Value("string"),
                     "caption": datasets.Value("string"),
                     "image": datasets.Image(),
-                    "start_time": datasets.Value("timestamp[s]"),
                     "clicks_path": datasets.Sequence(datasets.Sequence(datasets.Value("int32"), length=2)),
                     "clicks_time": datasets.Sequence(datasets.Value("timestamp[s]"))
                 }
@@ -154,7 +137,6 @@ class CapMIT1003(datasets.GeneratorBasedBuilder):
                     "usr_uid": pair.usr_uid,
                     "image": pair.img_path,
                     "caption": pair.caption,
-                    "start_time": pair.start_time,
                     "clicks_path": xy_coordinates,
                     "clicks_time": clicks_time
                 }

README.md

@@ -0,0 +1,56 @@
+---
+dataset_info:
+  features:
+  - name: obs_uid
+    dtype: string
+  - name: usr_uid
+    dtype: string
+  - name: caption
+    dtype: string
+  - name: image
+    dtype: image
+  - name: clicks_path
+    sequence:
+      sequence: int32
+      length: 2
+  - name: clicks_time
+    sequence: timestamp[s]
+  splits:
+  - name: train
+    num_bytes: 1611467
+    num_examples: 3848
+  download_size: 241443505
+  dataset_size: 1611467
+---
+
+### Dataset  Description
+CapMIT1003 is a dataset of captions and click-contingent image explorations collected during captioning tasks. 
+CapMIT1003 is based on the same stimuli from the well-known MIT1003 benchmark, for which eye-tracking data
+under free-viewing conditions is available, which offers a promising opportunity to concurrently study human attention under both tasks.
+
+
+### Usage
+You can load CapMIT1003 as follows:
+
+
+```python
+from datasets import load_dataset
+
+capmit1003_dataset = load_dataset("azugarini/CAPMIT1003", trust_remote_code=True)
+print(capmit1003_dataset["train"][0]) #print first example
+```
+
+
+
+
+
+### Citation Information
+If you use this dataset in your research or work, please cite the following paper:
+
+```
+@article{zanca2023contrastive,
+  title={Contrastive Language-Image Pretrained Models are Zero-Shot Human Scanpath Predictors},
+  author={Zanca, Dario and Zugarini, Andrea and Dietz, Simon and Altstidl, Thomas R and Ndjeuha, Mark A Turban and Schwinn, Leo and Eskofier, Bjoern},
+  journal={arXiv preprint arXiv:2305.12380},
+  year={2023}
+```