Adding files

README.md

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----
-license: apache-2.0
----
+---
+license: apache-2.0
+task_categories:
+- image-text-to-text
+tags:
+- image
+language:
+- en
+size_categories:
+- 10K<n<100K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train/*
+  - split: val
+    path: data/val/*
+  - split: test
+    path: data/test/*
+---
+
+# Flick30K Narratives
+
+[Original Source](https://www.kaggle.com/datasets/hsankesara/flickr-image-dataset) | [Google Localized Narrative](https://google.github.io/localized-narratives/)
+
+## 📌 Introduction
+
+This dataset collects the images and annotations from the original Flickr30K and the annotations from the project [localized-narratives](https://github.com/google/localized-narratives)
+
+## 🙏 Acknowledgement
+
+All credits to the original Flickr30K project and the localized-narratives teams.
+
+## 📜 Cite
+
+Please consider to cite the following related papers:
+
+```bibtex
+@article{young2014image,
+  title={From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions},
+  author={Young, Peter and Lai, Alice and Hodosh, Micah and Hockenmaier, Julia},
+  journal={Transactions of the Association for Computational Linguistics},
+  volume={2},
+  pages={67--78},
+  year={2014},
+  publisher={MIT Press}
+}
+
+@inproceedings{PontTuset_eccv2020,
+  author    = {Jordi Pont-Tuset and Jasper Uijlings and Soravit Changpinyo and Radu Soricut and Vittorio Ferrari},
+  title     = {Connecting Vision and Language with Localized Narratives},
+  booktitle = {ECCV},
+  year      = {2020}
+}
+```

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make_flickr_ds.py

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+import json
+import os
+from tqdm import tqdm
+
+from datasets import Dataset, load_dataset, Image
+import pandas as pd
+
+
+def load_jsonl(file_path):
+    """
+    Loads a JSONL file and returns a list of Python dictionaries.
+    Each dictionary represents a JSON object from a line in the file.
+    """
+    data = []
+    with open(file_path, 'r', encoding='utf-8') as f:
+        for line in f:
+            try:
+                # Parse each line as a JSON object
+                json_object = json.loads(line.strip())
+                data.append(json_object)
+            except json.JSONDecodeError as e:
+                print(f"Error decoding JSON on line: {line.strip()} - {e}")
+    return data
+
+
+def main():
+    dsets = ["train", "val", "test"]  # test val
+    workdir = "./flickr30k"
+
+    # Load annotations
+    annot_fn = os.path.join(workdir, "results.csv")
+    df = pd.read_csv(annot_fn, delimiter="|")
+    df = pd.DataFrame(df)
+
+    datadict = {}
+    for _, row in df.iterrows():
+        idx = row["image_name"].replace(".jpg", "")
+        if idx not in datadict:
+            datadict[idx] = {
+                "image_name": row["image_name"],
+                "image": os.path.join(workdir, "flickr30k_images", row["image_name"]),
+                "sentids": [],
+                "split": None,
+                "caption": [],
+                "narratives": []
+            }
+
+        datadict[idx]["sentids"].append(row[" comment_number"])
+        datadict[idx]["caption"].append(row[" comment"])
+
+    # Align to narratives splits.
+    for split in dsets:
+        narr = load_jsonl(os.path.join(workdir, "narratives", f"flickr30k_{split}_captions.jsonl"))
+        for item in narr:
+            idx = item["image_id"]
+            datadict[idx]["split"] = split
+            datadict[idx]["narratives"].append(item["caption"])
+
+    # make datasets
+    for split in dsets:
+        df = pd.DataFrame.from_dict(datadict, orient="index")
+        df = df[df["split"] == split]
+        ds = Dataset.from_pandas(df)
+        ds = ds.remove_columns(["__index_level_0__", "split"])
+        ds = ds.cast_column("image", Image())
+        ds.save_to_disk(os.path.join(workdir, "datasets", "data", split), max_shard_size="400MB")
+
+    return
+
+
+def test_dataset():
+    ds = load_dataset("./flickr30k/datasets")  # , split="val"
+    print(ds["train"][0])
+    
+
+if __name__ == "__main__":
+    # main()
+    test_dataset()