put back the building script

README.md

@@ -3,5 +3,5 @@ license: cc
 ---
 
 YFCC15m dataset from <https://github.com/openai/CLIP/blob/main/data/yfcc100m.md>.
-The subset is obtained by filtering the original YFCC100m (yfcc100m_dataset.sql) usin the photo ids from <https://github.com/openai/CLIP/blob/main/data/yfcc100m.md>.
+The subset is obtained by filtering the original YFCC100m (yfcc100m_dataset.sql) using the photo ids from <https://github.com/openai/CLIP/blob/main/data/yfcc100m.md>.
 The script to rebuild the data from the original YFCC100m is provided at [build_yfcc15m.py](build_yfcc15m.py).

build_yfcc15m.py

@@ -0,0 +1,49 @@
+import pandas as pd
+import sqlite3
+from yfcc100m.convert_metadata import generate_rows_from_db
+from datadings.tools import yield_threaded
+from tqdm import tqdm
+df = pd.read_table(
+    "yfcc100m_subset_data.tsv", header=None, names=["line_number", "identifier", "hash"]
+)
+clip_photoids = df["identifier"].tolist()  # 15 million ids
+rows = generate_rows_from_db("yfcc100m_dataset.sql", "yfcc100m_dataset")
+gen = yield_threaded(rows)
+dfs = []
+chunk = []
+for i, row in enumerate(tqdm(gen, total=100000000)):
+    chunk.append(row)
+    if (i + 1) % 10_000_000 == 0:
+        df = pd.DataFrame(
+            chunk,
+            columns=[
+                "photoid",
+                "uid",
+                "unickname",
+                "datetaken",
+                "dateuploaded",
+                "capturedevice",
+                "title",
+                "description",
+                "usertags",
+                "machinetags",
+                "longitude",
+                "latitude",
+                "accuracy",
+                "pageurl",
+                "downloadurl",
+                "licensename",
+                "licenseurl",
+                "serverid",
+                "farmid",
+                "secret",
+                "secretoriginal",
+                "ext",
+                "marker",
+            ],
+        )
+        df = df[df["photoid"].isin(clip_photoids)].reset_index(drop=True)
+        dfs.append(df)
+        chunk = []
+df = pd.concat(dfs)
+df.to_csv("yfcc15m.csv", index=False)