File size: 3,589 Bytes
5f5554c b10648a 5f5554c b10648a 5f5554c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | ---
license: apache-2.0
task_categories:
- image-classification
- zero-shot-image-classification
language:
- bg
- cs
- el
- hr
- hu
- pl
- ro
- sk
tags:
- e-commerce
- duplicate-detection
- entity-resolution
- multimodal
size_categories:
- 1M<n<10M
configs:
- config_name: images
default: true
data_files:
- split: train
path: images/shard-*.parquet
- config_name: items
data_files:
- split: train
path: items_train.csv
- split: test
path: items_test.csv
- config_name: splits
data_files:
- split: train
path: train_split.csv
- split: validation
path: val_split.csv
- config_name: groups
data_files:
- split: g5
path: groups_5.csv
- split: g6
path: groups_6.csv
- split: g7
path: groups_7.csv
- split: g8
path: groups_8.csv
- split: g9
path: groups_9.csv
- split: g10
path: groups_10.csv
- split: hard_5
path: groups_hard_5.csv
- split: category_5
path: groups_category_5.csv
---
# GLAMI Duplication Detection
Product-duplicate detection over GLAMI e-commerce listings: ~1.3M product
images plus multilingual titles, descriptions and attributes, with labelled
groups of items that do or do not refer to the same physical product.
Released under the Apache License 2.0 — see [LICENSE](LICENSE).
> TODO: describe how the labels were produced.
## Structure
| Config | Files | Contents |
| --- | --- | --- |
| `images` | `images/shard-*.parquet` | `itemId` → image bytes, one row per product image |
| `items` | `items_train.csv`, `items_test.csv` | `itemId`, `price`, `colorTagIdsString`, `departmentIds`, `brandEditionTagId`, `title`, `description`, `geo`, and `label` on train |
| `splits` | `train_split.csv`, `val_split.csv` | train/validation partition of the training items |
| `groups` | `groups_*.csv` | `item1..itemN`, `label` — candidate groups of 5–10 items, plus `hard_` and `category_` adversarial variants |
Images are keyed by `itemId` alone and are not pre-split. Every other file
references `itemId`, so all of them join against the same image table and no
picture is stored twice.
## Usage
```python
from datasets import load_dataset
images = load_dataset("zidcenek/GLAMI-Entity-Matching-Dataset", "images", split="train")
items = load_dataset("zidcenek/GLAMI-Entity-Matching-Dataset", "items", split="train")
groups = load_dataset("zidcenek/GLAMI-Entity-Matching-Dataset", "groups", split="g5")
images[0]["image"] # PIL.Image, decoded lazily
images[0]["itemId"]
```
The image config is ~TODO GB, so stream it if you do not want a full local copy:
```python
images = load_dataset(
"zidcenek/GLAMI-Entity-Matching-Dataset", "images", split="train", streaming=True
)
```
To attach images to items, build the `itemId` → row-index map once and index into it:
```python
index = {item_id: i for i, item_id in enumerate(images["itemId"])}
row = images[index[items[0]["itemId"]]]
```
## Reproducing the image shards
```bash
pip install -U datasets Pillow "huggingface_hub[hf_xet]"
python scripts/build_image_shards.py --inspect # check the filename -> itemId mapping
python scripts/build_image_shards.py # glami_images.tar.gz -> images/shard-*.parquet
hf auth login
python scripts/upload_to_hub.py # push to the Hub over HTTP
```
Both scripts are resumable: rerun the same command after an interruption.
See the header of each for the full options.
## Citation
> TODO
|