---
dataset_info:
  features:
  - name: item_id
    dtype: int64
  - name: image_id
    dtype: int64
  - name: geo
    dtype: string
  - name: name
    dtype: string
  - name: description
    dtype: string
  - name: category
    dtype: int64
  - name: category_name
    dtype: string
  - name: label_source
    dtype: string
  - name: image
    dtype: image
  splits:
  - name: train
    num_bytes: 10129258280.460548
    num_examples: 892803
  - name: test
    num_bytes: 2579288717.7234526
    num_examples: 223201
  download_size: 12362292055
  dataset_size: 12708546998.184002
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
size_categories:
- 1M<n<10M
---
### This is fork of original dataset converted to `dataset` format.

![GLAMI-1M Image](https://raw.githubusercontent.com/glami/glami-1m/main/media/glami-1m-dataset-examples.png)


GLAMI-1M contains 1.1 million fashion items, 968 thousand unique images and 1 million unique texts. It contains 13 languages, mostly European. And 191 fine-grained categories, for example we have 15 shoe types. It contains high quality annotations from professional curators and it also presents a difficult production industry problem.

Each sample contains an image, country code, name in corresponding language, description, target category and source of the label which can be of multiple types, it can be human or rule-based but most of the samples are human-based labels.


Read more on [GLAMI-1M home page at GitHub](https://github.com/glami/glami-1m)