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"""Visual similarities discovery (VSD) is an important task
with broad e-commerce applications. Given an image of
a certain object, the goal of VSD is to retrieve images of
different objects with high perceptual visual similarity. Al-
though being a highly addressed problem, the evaluation
of proposed methods for VSD is often based on a proxy of
an identification-retrieval task, evaluating the ability of a
model to retrieve different images of the same object. We
posit that evaluating VSD methods based on identification
tasks is limited, and faithful evaluation must rely on expert
annotations. In this paper, we introduce the first large-scale
fashion visual similarity benchmark dataset, consisting of
more than 110K expert-annotated image pairs."""
import csv
import json
import os
from typing import Optional, Union
import datasets
from pathlib import Path
from datasets.data_files import DataFilesDict
from datasets.features import Features
from datasets.info import DatasetInfo
from huggingface_hub import snapshot_download
# TODO: Add BibTeX citation
# Find for instance the citation on arxiv or on the dataset repo/website
_CITATION = """\
@InProceedings{huggingface:dataset,
title = {A great new dataset},
author={huggingface, Inc.
},
year={2020}
}
"""
_DESCRIPTION = """\
Visual similarities discovery (VSD) is an important task
with broad e-commerce applications. Given an image of
a certain object, the goal of VSD is to retrieve images of
different objects with high perceptual visual similarity. Al-
though being a highly addressed problem, the evaluation
of proposed methods for VSD is often based on a proxy of
an identification-retrieval task, evaluating the ability of a
model to retrieve different images of the same object. We
posit that evaluating VSD methods based on identification
tasks is limited, and faithful evaluation must rely on expert
annotations. In this paper, we introduce the first large-scale
fashion visual similarity benchmark dataset, consisting of
more than 110K expert-annotated image pairs.
"""
# TODO: Add a link to an official homepage for the dataset here
_HOMEPAGE = "https://vsd-benchmark.github.io/vsd/"
# TODO: Add the licence for the dataset here if you can find it
_LICENSE = "MIT"
_URL = "https://huggingface.co/datasets/vsd-benchmark/vsd-fashion/tree/main"
_HF_DATASET_ID = 'vsd-benchmark/vsd-fashion'
class VSDFashionConfig(datasets.BuilderConfig):
"""BuilderConfig for VSDFashion."""
def __init__(self, dataset_folder, split_folder, image_folder=None, **kwargs):
"""BuilderConfig for VSDFashion.
Args:
**kwargs: keyword arguments forwarded to super.
"""
# Version history:
# 0.0.21: Initial version.
super(VSDFashionConfig, self).__init__(version=datasets.Version("0.0.1"), **kwargs)
self.dataset_folder = dataset_folder
self.split_folder = split_folder
self.image_folder = image_folder
# TODO: Name of the dataset usually matches the script name with CamelCase instead of snake_case
class VSDFashion(datasets.GeneratorBasedBuilder):
def __init__(
self,
cache_dir: Optional[str] = None,
dataset_name: Optional[str] = None,
config_name: Optional[str] = None,
hash: Optional[str] = None,
base_path: Optional[str] = None,
info: Optional[DatasetInfo] = None,
features: Optional[Features] = None,
token: Optional[Union[bool, str]] = None,
use_auth_token="deprecated",
repo_id: Optional[str] = None,
data_files: Optional[Union[str, list, dict, DataFilesDict]] = None,
data_dir: Optional[str] = None,
storage_options: Optional[dict] = None,
writer_batch_size: Optional[int] = None,
name="deprecated",
image_folder: str = None,
**config_kwargs):
super().__init__(cache_dir, dataset_name, config_name, hash, base_path, info, features, token, use_auth_token, repo_id, data_files, data_dir, storage_options, writer_batch_size, name, **config_kwargs)
self.image_folder = Path(image_folder)
VERSION = datasets.Version("1.1.0")
BUILDER_CONFIGS = [
VSDFashionConfig(
name="in_catalog_retrieval_zero_shot",
description="Zero shot (no training) on fashion catalog query and candidates visual similairty",
dataset_folder='in_fashion',
split_folder='gt_tagging',
),
VSDFashionConfig(
name="in_catalog_open_catalog",
description="Training task for VSD where the queries in the train and test may overlap.",
dataset_folder='in_fashion',
split_folder='gt_tagging_split_open_catalog',
),
VSDFashionConfig(
name="in_catalog_closed_catalog",
description="Training task for VSD where the queries in the train and test DO NOT overlap.",
dataset_folder='in_fashion',
split_folder='gt_tagging_split_closed_catalog',
),
VSDFashionConfig(
name="consumer-catalog_wild_zero_shot",
description="Zero shot task for matching a consumer taken photo of a clothing and visually matching with a catalog item. Pretraining on any data is allowed, except consumer photos (queries).",
dataset_folder='in_fashion',
split_folder='gt_tagging_wild',
),
]
DEFAULT_CONFIG_NAME = "in_catalog_retrieval_zero_shot"
def _info(self):
features = datasets.Features(
{
"query": datasets.Image(),
"candidate": datasets.Image(),
"value": datasets.ClassLabel(num_classes=2, names=["neg", "pos"]),
}
)
return datasets.DatasetInfo(
# This is the description that will appear on the datasets page.
description=_DESCRIPTION,
# This defines the different columns of the dataset and their types
features=features, # Here we define them above because they are different between the two configurations
# If there's a common (input, target) tuple from the features, uncomment supervised_keys line below and
# specify them. They'll be used if as_supervised=True in builder.as_dataset.
# supervised_keys=("query", "candidate", "value"),
# Homepage of the dataset for documentation
homepage=_HOMEPAGE,
# License for the dataset if available
license=_LICENSE,
# Citation for the dataset
citation=_CITATION,
)
def _split_generators(self, dl_manager):
dataset_root_path = Path(snapshot_download(_HF_DATASET_ID, repo_type='dataset'))
dataset_path = dataset_root_path/self.config.dataset_folder
task_data_path = dataset_path/self.config.split_folder
if self.config.name == 'in_catalog_retrieval_zero_shot':
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"manifest_file": task_data_path/'manifest.json',
"seeds_file": task_data_path/'seeds.json',
"annotations_file": task_data_path/'in_fashion_tags_dict.json',
"split": "test",
"images_folder": self.image_folder,
"metadata_file": dataset_path/'dataset_metadata.json'
},
),
]
elif self.config.name == 'consumer-catalog_wild_zero_shot':
return [
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"manifest_file": task_data_path/'manifest.json',
"seeds_file": None,
"annotations_file": task_data_path/'in_fashion_outshop_tags_dict.json',
"split": "test",
"images_folder": self.image_folder,
"metadata_file": dataset_path/'dataset_metadata_wild.json',
"gallery_phases": ['train']
},
),
]
else:
return [
datasets.SplitGenerator(
name=datasets.Split.TRAIN,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"manifest_file": task_data_path/'manifest_train.json',
"seeds_file": task_data_path/'seeds_train.json',
"annotations_file": task_data_path/'in_fashion_tags_dict_train.json',
"split": "train",
"images_folder": self.image_folder,
"metadata_file": dataset_path/'dataset_metadata.json'
},
),
datasets.SplitGenerator(
name=datasets.Split.TEST,
# These kwargs will be passed to _generate_examples
gen_kwargs={
"manifest_file": task_data_path/'manifest_test.json',
"seeds_file": task_data_path/'seeds_test.json',
"annotations_file": task_data_path/'in_fashion_tags_dict_test.json',
"split": "test",
"images_folder": self.image_folder,
"metadata_file": dataset_path/'dataset_metadata.json'
},
),
]
# method parameters are unpacked from `gen_kwargs` as given in `_split_generators`
def _generate_examples(self, annotations_file, **kwargs):
with open(annotations_file, encoding="utf-8") as f:
data = json.load(f)
for key, row in enumerate(data):
# Yields examples as (key, example) tuples
yield key, {
"query": str(self.image_folder/row['key'][0]),
"candidate": str(self.image_folder/row['key'][1]),
"value": row['value'],
} |