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---
task_categories:
- image-feature-extraction
---
# Google Image Malaysian Vehicle Dedup
Original dataset https://huggingface.co/datasets/malaysia-ai/crawl-google-image-malaysian-vehicle
Source code at https://github.com/mesolitica/malaysian-dataset/tree/master/vlm/dedup-malaysian-vehicle
## Dedup 70% similar
[dedup-0.7.jsonl](dedup-0.7.jsonl), total deduped 97598 images,
```
{'filename': 'train-00075-of-00165-c0ebcc169b1f62d2.parquet',
'keyword': '2021 Honda City 1.5 E',
'no': 2,
'selected_indices': [696,
702,
705,
707,
712,
716,
720,
723,
727,
732,
742,
745,
775,
779,
780,
787,
797,
817,
844,
876,
894,
898,
905,
917,
962,
965,
966,
988,
993,
995,
1000,
1009,
1012,
1015,
1016,
1029,
1044,
1049,
1054,
1077,
1086,
1096,
1131,
1174,
1185,
1188,
1198,
1208,
1216,
1217,
1219,
1223,
1229,
1237,
1247,
1253,
1274,
1276,
1286,
1305,
1314,
1347,
1348,
1353,
1355,
1401,
1412]}
```
- `filename` is the parquet file from the original repository.
- `selected_indices` is the index of dataframe of that filename.
## Embedding
We convert to embedding using https://huggingface.co/google/siglip-base-patch16-512, we use MosaicML for faster indexing,
```python
from streaming import MDSWriter
from streaming.base.format.mds.encodings import Encoding, _encodings
from streaming import LocalDataset
import streaming
import numpy as np
from tqdm import tqdm
class Float32(Encoding):
def encode(self, obj) -> bytes:
return obj.tobytes()
def decode(self, data: bytes):
return np.frombuffer(data, np.float32)
_encodings['float32'] = Float32
dataset = LocalDataset('embedding')
``` |