datasetId
stringlengths 2
117
| author
stringlengths 2
42
⌀ | last_modified
timestamp[us, tz=UTC] | downloads
int64 0
9.36M
| likes
int64 0
3.89k
| tags
list | task_categories
list | createdAt
timestamp[us, tz=UTC] | card
stringlengths 19
1.01M
| embedding
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|---|---|---|---|---|---|---|---|---|---|
DBQ/Dior.Product.prices.China
|
DBQ
| 2023-11-19T08:58:24Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Dior",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T08:58:19Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: China - Dior - Product-level price list
tags:
- webscraping
- ecommerce
- Dior
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1753160
num_examples: 4499
download_size: 534935
dataset_size: 1753160
---
# Dior web scraped data
## About the website
The **luxury fashion industry** in the **Asia Pacific region**, and in particular in **China**, is a rapidly expanding sector. As the middle class grows in wealth, there is an increasing demand for high-end goods from prestigious brands such as Christian **Dior**. The sector has been greatly influenced by technological innovations, the most notable of which is **ecommerce**. In recent years, there has been a significant shift in consumer behavior with more people opting to shop online for their luxury items. The dataset observed contains **Ecommerce product-list page (PLP) data** on Dior in China, providing valuable insights into market trends and consumer preferences.
## Link to **dataset**
[China - Dior - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Dior%20Product-prices%20China/r/recjNQrDONn090TZG)
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DBQ/Prada.Product.prices.United.Kingdom
|
DBQ
| 2023-11-19T08:58:37Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Prada",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T08:58:32Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United Kingdom - Prada - Product-level price list
tags:
- webscraping
- ecommerce
- Prada
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1114680
num_examples: 2180
download_size: 327100
dataset_size: 1114680
---
# Prada web scraped data
## About the website
Prada operates in the **luxury fashion industry** within the **EMEA** region, emphasizing particularly in the **United Kingdom**. The luxury fashion industry in the UK has a considerable potential market for Prada since it is well-known for its affluent consumer base. This industry has witnessed significant growth due to the rise in online shopping, thereby boosting the Luxury Ecommerce market segment. Our dataset displays **Ecommerce product-list page (PLP) data** specifically around the Pradas offerings in the UK. This data grants valuable insights, highlighting key market trends and consumer preferences within UKs luxury fashion industry.
## Link to **dataset**
[United Kingdom - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20United%20Kingdom/r/recbEzCcd6HPZZzLt)
|
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DBQ/Prada.Product.prices.Germany
|
DBQ
| 2023-11-19T08:58:50Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Prada",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T08:58:45Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Germany - Prada - Product-level price list
tags:
- webscraping
- ecommerce
- Prada
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1315247
num_examples: 2588
download_size: 373618
dataset_size: 1315247
---
# Prada web scraped data
## About the website
The **Luxury Fashion Industry** in the **EMEA** region, particularly in **Germany**, has experienced significant transformation in recent years. The face of the industry is continuously changing, with fashion houses like **Prada** sitting at the forefront of this evolution. With an increasing number of consumers turning to online platforms for shopping, the digitalization process has been greatly accelerated for high-end fashion labels. The **ecommerce** sector has thus become instrumental in driving sales. The dataset observed includes **Ecommerce product-list page (PLP) data** on **Prada** in Germany, offering a fresh perspective on buying habits, consumer behaviour, and fashion trends within this evolving industry in the region.
## Link to **dataset**
[Germany - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Germany/r/recTwfXDGd6805PNU)
|
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] |
Ark323/joilo
|
Ark323
| 2023-11-19T09:06:30Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T08:58:52Z |
---
license: openrail
---
|
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DBQ/Net.a.Porter.Product.prices.Russia
|
DBQ
| 2023-11-19T08:59:07Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T08:59:00Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Russia - Net-a-Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Net
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 16708490
num_examples: 41393
download_size: 5182135
dataset_size: 16708490
---
# Net-a-Porter web scraped data
## About the website
The **EMEA fashion industry**, particularly in **Russia**, has been experiencing substantial growth in online channels due to increased internet penetration and smartphone usage. A significant player in this advancement is **Net-a-Porter**. This platform belongs to the **luxury ecommerce industry**, offering a wide range of premium brands. With the shift towards digital platforms in the shopping behavior of consumers, **Net-a-porter** is making its remarkable presence. The dataset observed provides insight into their online activities, particularly the **Ecommerce product-list page (PLP) data** for Net-a-Porter in Russia. This provides key understanding into customer preferences, behavior, and potential market trends.
## Link to **dataset**
[Russia - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Russia/r/recjMpDIWAH8eIxfl)
|
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DBQ/Mr.Porter.Product.prices.South.Africa
|
DBQ
| 2023-11-19T08:59:23Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T08:59:16Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: South Africa - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 8982718
num_examples: 27354
download_size: 2192806
dataset_size: 8982718
---
# Mr Porter web scraped data
## About the website
The **E-commerce industry in EMEA**, particularly in **South Africa**, has seen substantial growth over the past years. **Mr Porter**, a prominent player in this space, has managed to tap into this market successfully. Online shopping has become more prevalent due to the increased internet penetration and the convenience it offers. South Africa, being one of the most advanced economies in Africa, has a sizeable online consumer base. The dataset observed includes **Ecommerce product-list page (PLP)** data on Mr Porter in South Africa, indicating the current online behavior patterns and preferences of online shoppers.
## Link to **dataset**
[South Africa - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20South%20Africa/r/rec4LQG4qR9XPMHVP)
|
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DBQ/Burberry.Product.prices.Japan
|
DBQ
| 2023-11-19T08:59:41Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Burberry",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T08:59:31Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Japan - Burberry - Product-level price list
tags:
- webscraping
- ecommerce
- Burberry
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 985467
num_examples: 2950
download_size: 267744
dataset_size: 985467
---
# Burberry web scraped data
## About the website
The **Fashion Industry** in the **Asia Pacific**, particularly in **Japan**, has seen a significant growth in recent years. High-end, luxury brands like **Burberry** have established themselves firmly in the region. Its a fast-paced, highly consumer-driven industry that heavily incorporates the latest technology and trends. One noteworthy trend in Japans fashion industry is the rapid expansion of **Ecommerce platforms**. The dataset observed provides valuable insights into the **Ecommerce product-list page (PLP) data** on Burberrys operations in Japan, highlighting the online shopping preferences and buying behaviors of consumers in this unique and highly evolved marketplace.
## Link to **dataset**
[Japan - Burberry - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Burberry%20Product-prices%20Japan/r/recxtv3fyaKGgEGOj)
|
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DBQ/Gucci.Product.prices.Sweden
|
DBQ
| 2023-11-19T08:59:57Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T08:59:51Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Sweden - Gucci - Product-level price list
tags:
- webscraping
- ecommerce
- Gucci
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 2345108
num_examples: 4916
download_size: 674510
dataset_size: 2345108
---
# Gucci web scraped data
## About the website
The **fashion industry** in the **EMEA** region, more specifically in **Sweden**, has seen a significant shift in recent years. With the surge in **digital transformation**, there has been remarkable growth in the **online luxury fashion market**, where premier brands like **Gucci** have amplified their presence. One particular focus area has been the **Ecommerce product-list pages (PLP)**, aiming to provide a seamless and immersive digital shopping experience. From an analysis of the dataset on Gucci’s PLP in Sweden, it’s clear that e-commerce has a pivotal role currently and will likely maintain this significance in shaping the future of luxury fashion within the region.
## Link to **dataset**
[Sweden - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Sweden/r/reckQ64odNXQly07Z)
|
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DBQ/My.Theresa.Product.prices.United.Kingdom
|
DBQ
| 2023-11-19T09:00:22Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"My Theresa",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:00:10Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United Kingdom - My Theresa - Product-level price list
tags:
- webscraping
- ecommerce
- My Theresa
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 34106714
num_examples: 97884
download_size: 10195453
dataset_size: 34106714
---
# My Theresa web scraped data
## About the website
My Theresa is a major player in the **Ecommerce** industry in **EMEA**, with a strong presence in the **United Kingdom**. The **online luxury Fashion** business has experienced significant growth over the years, driven by advancements in technology, rising disposable income, increased internet penetration, and changing consumer preferences. Ecommerce is ever-evolving; with an amalgamation of technological disruption, competitive dynamics, and a shift in consumer behavior making its impact felt on the Ecommerce landscape in the United Kingdom. **My Theresa** focuses on digitally enabled direct-to-consumer boutique experiences. The dataset observed has **Ecommerce product-list page (PLP) data** on My Theresa in the United Kingdom which gives a snapshot of its offerings.
## Link to **dataset**
[United Kingdom - My Theresa - Product-level price list dataset](https://www.databoutique.com/buy-data-page/My%20Theresa%20Product-prices%20United%20Kingdom/r/recPYZC2plm5PtCch)
|
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DBQ/Rue.La.La.Product.prices.United.States
|
DBQ
| 2023-11-19T09:00:40Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Rue La La",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:00:34Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United States - Rue La La - Product-level price list
tags:
- webscraping
- ecommerce
- Rue La La
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 2635237
num_examples: 7594
download_size: 514285
dataset_size: 2635237
---
# Rue La La web scraped data
## About the website
Rue La La operates in the thriving **Ecommerce industry** in the United States. This online-driven marketplace sector is significantly fuelled by the continuous innovations in technology, the growing adoption of mobile devices, and the changing consumption patterns of consumers. In particular, Rue La La is recognized for its flash sales model, selling designer apparels, accessories, footwear, and home decor among other things. As an integral part of the **online retail industry**, Rue La La leverages **Ecommerce product-list page (PLP) data** to create a personalized shopping experience, track customer behavior, and drive sales, thereby maximizing its growth opportunities within the American Ecommerce landscape.
## Link to **dataset**
[United States - Rue La La - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Rue%20La%20La%20Product-prices%20United%20States/r/recfmgzc6g12yIFjM)
|
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] |
DBQ/Chloe.Product.prices.France
|
DBQ
| 2023-11-19T09:01:01Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Chloe",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:00:53Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: France - Chloe - Product-level price list
tags:
- webscraping
- ecommerce
- Chloe
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 684003
num_examples: 2459
download_size: 155003
dataset_size: 684003
---
# Chloe web scraped data
## About the website
The **e-commerce industry** in EMEA, particularly in **France**, is increasingly competitive and dynamic. This sector experiences remarkable growth with the rise of digital platforms and evolving consumer behaviors. **Chloe**, operating in this market, offers an array of fashion items and luxury goods. The observed dataset provides crucial information about the **Ecommerce product-list page (PLP)** data on Chloe in France. This data reflects online consumer interactions, preferences, and purchasing patterns, providing valuable insight into the overall market position and performance of Chloe in Frances digital retail landscape. It underlines the potent potential of data analytics in shaping the future trajectories of e-commerce businesses.
## Link to **dataset**
[France - Chloe - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Chloe%20Product-prices%20France/r/reccacrLFdY2bqA41)
|
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DBQ/Net.a.Porter.Product.prices.Macao
|
DBQ
| 2023-11-19T09:01:25Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:01:14Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Macao - Net-a-Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Net
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 20989555
num_examples: 51420
download_size: 6378100
dataset_size: 20989555
---
# Net-a-Porter web scraped data
## About the website
The **Ecommerce industry** in the **Asia Pacific** region, specifically in **Macao**, demonstrates a growing digital environment with a high affinity for luxury retail consumption. As an online retail destination, **Net-a-Porter** contributes to this sector, carving out a niche market that is driven by affluent consumers who appreciate luxury fashion. The robust digital infrastructure coupled with high purchasing power in Macao provide an ideal environment for Net-a-Porter to thrive. The dataset observed contains **Ecommerce Product-List Page (PLP)** data on Net-a-Porter in Macao, offering insights into consumer behavior, preferences, and emerging trends in this vibrant sector.
## Link to **dataset**
[Macao - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Macao/r/recxhKe09GdefwSnj)
|
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DBQ/Loro.Piana.Product.prices.Germany
|
DBQ
| 2023-11-19T09:02:04Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Loro Piana",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:01:58Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Germany - Loro Piana - Product-level price list
tags:
- webscraping
- ecommerce
- Loro Piana
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 390968
num_examples: 1133
download_size: 130740
dataset_size: 390968
---
# Loro Piana web scraped data
## About the website
Loro Piana operates in the **luxury fashion industry** within the Europe, Middle East, and Africa (EMEA) region, with a strong market presence in **Germany**. The luxury fashion market in this region is characterized by a high demand for **high-quality materials, craftsmanship**, and **unique designs**. The industry has seen a substantial growth in **online retail**, or **Ecommerce**, with an increasing number of consumers turning to online platforms for convenience and a wider range of options. Specifically, the dataset observed has information about the **Ecommerce product-list page (PLP)** data for Loro Piana in Germany, offering insights into their online market performance.
## Link to **dataset**
[Germany - Loro Piana - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Loro%20Piana%20Product-prices%20Germany/r/recKScQaTx24JbIkH)
|
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DBQ/Net.a.Porter.Product.prices.Tunisia
|
DBQ
| 2023-11-19T09:02:27Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:02:17Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Tunisia - Net-a-Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Net
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 17289050
num_examples: 42405
download_size: 5424439
dataset_size: 17289050
---
# Net-a-Porter web scraped data
## About the website
The **Net-a-Porter** brand operates within the highly competitive **Ecommerce** industry in the **EMEA** region, specifically in **Tunisia**. This industry encapsulates all buying and selling of goods or services online, with fashion and luxury products being a significant contributor. The growth of ecommerce platforms in Tunisia has led to a surge in the digital marketplace, playing a key role in boosting the economy of the country. Our dataset primarily revolves around **Ecommerce product-list page (PLP)** data of this renowned online retailer in Tunisia, offering insight into online customer behaviour and product performance.
## Link to **dataset**
[Tunisia - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20Tunisia/r/recyrW7jHdjmHA1Cr)
|
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DBQ/Gucci.Product.prices.Germany
|
DBQ
| 2023-11-19T09:02:42Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:02:36Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Germany - Gucci - Product-level price list
tags:
- webscraping
- ecommerce
- Gucci
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 2610110
num_examples: 5418
download_size: 799670
dataset_size: 2610110
---
# Gucci web scraped data
## About the website
The **luxury fashion industry** in the **EMEA** region, especially in **Germany**, is significantly advanced and sophisticated, thanks to the embracing of digital technologies. **Gucci**, a prominent brand in this industry, has successfully penetrated the German market. The recently observed dataset is a collection of **Ecommerce product-list page (PLP) data** specifically on **Guccis operations in Germany**. The initiative is to study and understand the trends, consumer behavior, and transaction statistics associated with this luxury brand, in a digitally-inclined marketplace. This dataset provides valuable insights into Guccis online presence and performance in the German luxury industry.
## Link to **dataset**
[Germany - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Germany/r/recLAvHvPXYVDc76b)
|
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DBQ/Louis.Vuitton.Product.prices.France
|
DBQ
| 2023-11-19T09:02:59Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Louis Vuitton",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:02:53Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: France - Louis Vuitton - Product-level price list
tags:
- webscraping
- ecommerce
- Louis Vuitton
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 3471507
num_examples: 7806
download_size: 915905
dataset_size: 3471507
---
# Louis Vuitton web scraped data
## About the website
The **Luxury Fashion** industry in the **EMEA** region, specifically in **France**, is a competitive and dynamic sector representing some of the most prestigious names in fashion. **Louis Vuitton**, a prominent player, is renowned for its high-end products, setting the tone for luxury retail within and beyond France. The recent rise in digital transformation has intensified the focus on **Ecommerce** within this industry. The dataset in review contains **Ecommerce product-list page (PLP) data** on Louis Vuitton in France. This data provides valuable insights into consumer behaviour, purchasing patterns, product preferences, and overall performance metrics of Louis Vuittons online retail.
## Link to **dataset**
[France - Louis Vuitton - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Louis%20Vuitton%20Product-prices%20France/r/recuJmM9pHnEywNtQ)
|
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] |
DBQ/Farfetch.Product.prices.Switzerland
|
DBQ
| 2023-11-19T09:04:46Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Farfetch",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:03:49Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Switzerland - Farfetch - Product-level price list
tags:
- webscraping
- ecommerce
- Farfetch
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 220235667
num_examples: 587954
download_size: 77809281
dataset_size: 220235667
---
# Farfetch web scraped data
## About the website
**Farfetch** operates within the highly competitive **e-commerce industry** in **EMEA**, particularly in Switzerland. This industry has profound influence largely due to the increased online shopping trends, escalated by COVID-19 pandemic. It boasts a diverse portfolio of businesses specialising in various sectors, including retail, fashion, electronics, and home décor, among others. Switzerland, widely recognised for its affluent consumer base, sophisticated infrastructure, and favourable legislative environment, offers an ideal market for e-commerce activities. The dataset observed specifically contains **Ecommerce product-list page (PLP)** data related to **Farfetch** operations in **Switzerland**. This data provides valuable insights into market trends, customer behaviour, and product performance within this geographical market.
## Link to **dataset**
[Switzerland - Farfetch - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Farfetch%20Product-prices%20Switzerland/r/rec29qujpLBbDdgET)
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] |
DBQ/Gucci.Product.prices.South.Korea
|
DBQ
| 2023-11-19T09:05:02Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:04:56Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: South Korea - Gucci - Product-level price list
tags:
- webscraping
- ecommerce
- Gucci
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1709874
num_examples: 3796
download_size: 490827
dataset_size: 1709874
---
# Gucci web scraped data
## About the website
In the flourishing **fashion and luxury goods industry** in the Asia Pacific region, particularly **South Korea**, high-end brands like **Gucci** are experiencing a surge in popularity. E-commerce is a major driving force; this upward trend reflected in the dataset gathered, which includes **Ecommerce product-list page (PLP)** data on Guccis presence in South Korea. The country’s exponential rise in individual wealth and the strong influence of pop culture on fashion trends have paved the way for an increase in consumer demand. This illustrates the growing appreciation for luxury fashion brands in the Asia Pacific region.
## Link to **dataset**
[South Korea - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20South%20Korea/r/recvqhieM1soAYRdL)
|
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DBQ/Chanel.Product.prices.Germany
|
DBQ
| 2023-11-19T09:05:17Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Chanel",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:05:12Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Germany - Chanel - Product-level price list
tags:
- webscraping
- ecommerce
- Chanel
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 777798
num_examples: 1428
download_size: 199720
dataset_size: 777798
---
# Chanel web scraped data
## About the website
In the EMEA region, particularly in **Germany**, the luxury fashion industry greatly evolved over the years. The **luxury fashion industry** features renowned brands such as **Chanel**, among others. Consumers in Germany exhibit a strong appetite for high-end fashion, thereby intensifying the competition in this sector. With the advent of digitization, many of these high-end brands have shifted to online platforms. Especially during the COVID-19 pandemic, **Ecommerce** has become pivotal in this industry. The dataset observed comprises the **Ecommerce product-list page (PLP) data** on Chanels product availability, prices, and sales in Germany, providing a detailed perspective of the brands digital market status.
## Link to **dataset**
[Germany - Chanel - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Chanel%20Product-prices%20Germany/r/recTZkFTUHIvrjoB3)
|
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DBQ/Loro.Piana.Product.prices.China
|
DBQ
| 2023-11-19T09:05:30Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Loro Piana",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:05:25Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: China - Loro Piana - Product-level price list
tags:
- webscraping
- ecommerce
- Loro Piana
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 168135
num_examples: 817
download_size: 35142
dataset_size: 168135
---
# Loro Piana web scraped data
## About the website
The **luxury fashion industry** is an influential sector in the **Asia Pacific**, particularly in **China**, which exhibits an increasing influence on the global landscape. It is driven by rising consumer demand for **high-end fashion** and **luxury goods**. With the advent of technology, the e-commerce platform has become a new playground for luxury brands like **Loro Piana**. **E-commerce** in China provides an opportunity for these brands to extend its reach to its customers. In this context, we have the **Ecommerce product-list page (PLP) data** available for **Loro Piana** in China, which provides comprehensive insights into consumer behaviors, preferences, and the performance of the brand in the digital marketplace.
## Link to **dataset**
[China - Loro Piana - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Loro%20Piana%20Product-prices%20China/r/recR7x5CkoSXdIOjH)
|
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DBQ/Burberry.Product.prices.China
|
DBQ
| 2023-11-19T09:05:45Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Burberry",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:05:39Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: China - Burberry - Product-level price list
tags:
- webscraping
- ecommerce
- Burberry
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 638473
num_examples: 2014
download_size: 190988
dataset_size: 638473
---
# Burberry web scraped data
## About the website
The **luxury fashion industry** in the Asia Pacific region, particularly in **China**, has seen a significant shift towards digitalization. **Online shopping**, fuelled by the growth of **Ecommerce**, has become a major sales channel for high-end labels like **Burberry**. This growth in online sales has outpaced that of the offline sector, making e-commerce a key driver for the luxury fashion sector. Chinese consumption of luxury goods is turning towards **e-commerce platforms**, which acts as a crucial bridge connecting luxury fashion powerhouses and customers. The dataset examined contains **Ecommerce product-list page (PLP) data** on **Burberry** within the **Chinese market**.
## Link to **dataset**
[China - Burberry - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Burberry%20Product-prices%20China/r/rec8WRWvzC4DWfhsL)
|
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DBQ/Mr.Porter.Product.prices.Hong.Kong
|
DBQ
| 2023-11-19T09:06:02Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:05:55Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Hong Kong - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 8934085
num_examples: 27206
download_size: 2064760
dataset_size: 8934085
---
# Mr Porter web scraped data
## About the website
Mr Porter operates within the **Ecommerce Fashion Retail** industry, one thats seeing a dynamic surge in the **Asia Pacific** region, particularly in **Hong Kong**. This metropolis, known for its fashion-forward populace and high internet penetration, boasts a thriving Ecommerce scene. Young, tech-savvy consumers are driving growth in online shopping, appreciating the ease and variety it provides. As per our dataset, we have specifically observed **Ecommerce product-list page (PLP) data** for **Mr Porter in Hong Kong**. This data provides valuable insights into market trends, consumer preferences, and competitive dynamics, all crucial for strategizing in this vibrant digital retail landscape.
## Link to **dataset**
[Hong Kong - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Hong%20Kong/r/reccGQkaol1aca5fH)
|
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DBQ/Gucci.Product.prices.Romania
|
DBQ
| 2023-11-19T09:06:17Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:06:11Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Romania - Gucci - Product-level price list
tags:
- webscraping
- ecommerce
- Gucci
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 2466121
num_examples: 5169
download_size: 723248
dataset_size: 2466121
---
# Gucci web scraped data
## About the website
Operating within the **luxury fashion industry**, **Gucci** marks its prominent presence in the EMEA region, specifically in **Romania**. The luxury fashion industry in Romania has shown considerable growth, owing to increased consumer spending and changing lifestyle trends. The industry is characterized by high-income consumers with a taste for luxury fashion products displaying their status and personality. The industry, driven by premium quality and exclusiveness, demonstrates a good deal of opportunities for luxury brands like Gucci. The dataset observed provides insightful **Ecommerce product-list page (PLP) data** region specific information on Gucci in the Romanian marketplace.
## Link to **dataset**
[Romania - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Romania/r/recfSStZ86lHvxQZi)
|
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DBQ/Net.a.Porter.Product.prices.South.Korea
|
DBQ
| 2023-11-19T09:06:36Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:06:28Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: South Korea - Net-a-Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Net
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 20928720
num_examples: 51265
download_size: 6412160
dataset_size: 20928720
---
# Net-a-Porter web scraped data
## About the website
The **fashion e-commerce industry** in the Asia Pacific, particularly in **South Korea**, has seen tremendous growth over the last few years. Rapid advancements in **digital technology**, together with a sophisticated logistics infrastructure and increasingly affluent consumer base, are driving this expansion. **South Korea’s digital infrastructure** is one of the most advanced in the region, offering online retailers access to a broad, tech-savvy audience. Cultural factors like the Korean Wave (“Hallyu”) are also influencing fashion trends and consumption patterns. The dataset observed provides **e-commerce product-list page (PLP) data** about **Net-a-Porter** in South Korea, shedding light on purchasing patterns and preferences in this vibrant market.
## Link to **dataset**
[South Korea - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20South%20Korea/r/recbd28ziED1dxjSE)
|
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DBQ/Mr.Porter.Product.prices.Romania
|
DBQ
| 2023-11-19T09:06:54Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:06:47Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Romania - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 9143455
num_examples: 27826
download_size: 2082688
dataset_size: 9143455
---
# Mr Porter web scraped data
## About the website
Mr Porter operates in the expansive and rapidly evolving **Ecommerce industry** in EMEA, specifically within the fast-growing Romanian market. This sector has gained significant traction owing to the convergence of technology and commerce, encompassing a broad range of online business activities for products and services. **Romania** has emerged as a key player, given its robust digital infrastructure and tech-savvy population. The data set examined provides a comprehensive insight into **Ecommerce product-list page (PLP)** data for **Mr Porter** in this blooming market. Through this, one can decipher key trends in consumer behaviour, product preferences, and overall market trajectory.
## Link to **dataset**
[Romania - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Romania/r/recHYAkrDtNZwmVbJ)
|
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DBQ/Farfetch.Product.prices.South.Korea
|
DBQ
| 2023-11-19T09:08:39Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Farfetch",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:07:34Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: South Korea - Farfetch - Product-level price list
tags:
- webscraping
- ecommerce
- Farfetch
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 212179509
num_examples: 569613
download_size: 77331883
dataset_size: 212179509
---
# Farfetch web scraped data
## About the website
The **Ecommerce industry** in the **Asia Pacific** region, particularly in **South Korea**, has seen dramatic growth over the recent years. Known for its tech-savvy population and high internet penetration, South Korea is considered as one of the world’s largest and most mature Ecommerce markets. The dataset observed sheds light on the Ecommerce **Product-List Page (PLP)** data on **Farfetch** in South Korea. The company operates within the luxury fashion sector, serving as a platform for boutiques and brands to sell their products, and is an important player in the Korean Ecommerce landscape. Growth in this sector can be attributed to the increasing demand for branded and luxury items in the nation.
## Link to **dataset**
[South Korea - Farfetch - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Farfetch%20Product-prices%20South%20Korea/r/recKBupvuFtQd25fO)
|
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] |
DBQ/Fendi.Product.prices.United.States
|
DBQ
| 2023-11-19T09:08:54Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Fendi",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:08:48Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United States - Fendi - Product-level price list
tags:
- webscraping
- ecommerce
- Fendi
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1009889
num_examples: 2897
download_size: 306452
dataset_size: 1009889
---
# Fendi web scraped data
## About the website
The **luxury fashion industry** in the **United States** serves as a prominent platform for world-renowned brands like **Fendi**. The industry presents a vibrant mix of branded apparel, footwear, accessories, jewelry and personal luxury goods. A key aspect is the significant surge of **ecommerce**, revolutionizing traditional retail with its superior convenience and selection diversity. In this digital landscape, emphasis is placed on data-driven strategies, where datasets, like **Ecommerce product-list page (PLP) data** on Fendi, provide crucial insights for decision-making and effective market reach. The fashion industry in America, laced with digital innovation, presents significant opportunities and challenges in equal measure.
## Link to **dataset**
[United States - Fendi - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Fendi%20Product-prices%20United%20States/r/reclz9iHklMaBqZJ0)
|
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DBQ/Chanel.Product.prices.United.States
|
DBQ
| 2023-11-19T09:09:12Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Chanel",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:09:02Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United States - Chanel - Product-level price list
tags:
- webscraping
- ecommerce
- Chanel
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 748670
num_examples: 1452
download_size: 189766
dataset_size: 748670
---
# Chanel web scraped data
## About the website
The global luxury goods industry, specifically the high-end fashion sector, is a competitive marketplace where brands like **Chanel** thrive. The American market, especially the **United States**, plays a critical role in this industry, as it is one of the worlds biggest consumers of luxury products. With its affluent consumers propensity for luxury and up-scale products, the US market is a major driver of growth in this sector. The recent shift towards **Ecommerce** and digital platforms has further revolutionized luxury sales. The observed dataset provides valuable **Ecommerce product-list page (PLP)** data on Chanels operations in the United States, highlighting key trends and insights into their online strategies and performance.
## Link to **dataset**
[United States - Chanel - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Chanel%20Product-prices%20United%20States/r/recMdZB18HDhbWplC)
|
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DBQ/Mr.Porter.Product.prices.Taiwan
|
DBQ
| 2023-11-19T09:09:28Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:09:23Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Taiwan - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 8970329
num_examples: 27315
download_size: 2161710
dataset_size: 8970329
---
# Mr Porter web scraped data
## About the website
The **Ecommerce industry** in Asia Pacific, particularly in **Taiwan**, has been burgeoning due to increased internet penetration and growing consumer trust in online transactions. The industry encompasses various businesses, including fashion retailers like **Mr Porter**. Recognized for its upscale menswear, Mr Porter has carved a good standing in the online shopping landscape of Taiwan. The observed dataset specifically covers Ecommerce product-list page (PLP) data, offering valuable insights into Mr Porters performance and competitive stance within Taiwan. As Ecommerce sees an upward trend in Taiwan, understanding PLP data becomes essential for carving successful market strategies.
## Link to **dataset**
[Taiwan - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Taiwan/r/rec2pFzgTY38uwa95)
|
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] |
DBQ/Louis.Vuitton.Product.prices.Australia
|
DBQ
| 2023-11-19T09:09:43Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Louis Vuitton",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:09:37Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Australia - Louis Vuitton - Product-level price list
tags:
- webscraping
- ecommerce
- Louis Vuitton
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 2779833
num_examples: 6525
download_size: 739720
dataset_size: 2779833
---
# Louis Vuitton web scraped data
## About the website
The **luxury fashion industry** in the **Asia Pacific**, particularly in **Australia**, has seen a considerable surge in popularity over recent years. With a central focus on high-end designer brands such as **Louis Vuitton**, this lucrative market operates both in physical boutiques and more predominantly, via **ecommerce**. Australian consumers are now highly targeted by luxury brand campaigns due to their increasing purchasing power. The dataset observed contains comprehensive **Ecommerce product-list page (PLP) data** specific to the operations of **Louis Vuitton in Australia**, offering valuable insight into market preferences, buying behaviour and product performance in the digital space.
## Link to **dataset**
[Australia - Louis Vuitton - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Louis%20Vuitton%20Product-prices%20Australia/r/recEN6BuMhUq7CTFa)
|
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DBQ/Fendi.Product.prices.Italy
|
DBQ
| 2023-11-19T09:09:57Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Fendi",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:09:51Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Italy - Fendi - Product-level price list
tags:
- webscraping
- ecommerce
- Fendi
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 806401
num_examples: 1995
download_size: 253225
dataset_size: 806401
---
# Fendi web scraped data
## About the website
Operating within the **luxury fashion industry**, **Fendi** is a notable player in the EMEA region, particularly in its home country, **Italy**. Known for its innovative Italian craftsmanship, it represents an essential pillar of the **Italian luxury goods sector**. The industry is characterized by artisanal production, prestigious brands, premium prices, and a global customer base focusing on exclusivity and quality. A significant trend in this industry is the shift towards **digital platforms** and **Ecommerce** to meet evolving consumer behaviours. Considering this context, the dataset reflects **Ecommerce product-list page (PLP) data on Fendi** in Italy, unveiling consumer preferences and trends in Italian luxury shopping.
## Link to **dataset**
[Italy - Fendi - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Fendi%20Product-prices%20Italy/r/recGB0fxVukeTKxdK)
|
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DBQ/Hermes.Product.prices.Italy
|
DBQ
| 2023-11-19T09:10:13Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Hermes",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:10:07Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Italy - Hermes - Product-level price list
tags:
- webscraping
- ecommerce
- Hermes
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: int64
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 196574
num_examples: 535
download_size: 49870
dataset_size: 196574
---
# Hermes web scraped data
## About the website
The **EMEA luxury fashion industry**, particularly in **Italy**, is characterized by its high-end, high-quality retail products. Italy is home to many iconic fashion brands and is one of the worlds fashion capitals. **Hermes** is one of the prominent players in this sphere. With its sophisticated designs and high-quality craftsmanship, Hermes caters to a luxury-oriented demographic. The retail industry, more specifically, is making strategic shifts towards **Ecommerce**, stepping up their game in the digital market. The dataset observed provides insight into this shift, featuring **Ecommerce product-list page (PLP)** data of Hermes in Italy. This data provides an insight into Hermes online market dynamics, signalling its efforts to adapt to the increasingly evolving digital shopping trends.
## Link to **dataset**
[Italy - Hermes - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Hermes%20Product-prices%20Italy/r/recHVPAiKIshqPYKD)
|
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] |
DBQ/Saint.Laurent.Standard.Categories.Italy
|
DBQ
| 2023-11-19T09:10:25Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Saint Laurent",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:10:21Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Italy - Saint Laurent - Fashion Standard Categories
tags:
- webscraping
- ecommerce
- Saint Laurent
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: dbq_prd_type
dtype: string
- name: competence_date
dtype: string
- name: website_name
dtype: string
- name: product_code
dtype: string
- name: tag_field
dtype: string
splits:
- name: train
num_bytes: 294430
num_examples: 3064
download_size: 51647
dataset_size: 294430
---
# Saint Laurent web scraped data
## About the website
Saint Laurent operates within the vibrant **luxury fashion industry** in the **EMEA** region, particularly in **Italy**. The Italian luxury fashion sector is characterised by its rich history, renowned craftsmanship and a high demand for its prestigious brands. **Saint Laurent** falls under this prestigious category. The industry has significant presence online, propelled by the rise of **e-commerce** and digital marketing strategies. The dataset observed pertains to **e-commerce product tags data** for Saint Laurent in Italy. This data illustrates the strong digital footprint in the Italian market and is instrumental for fashion trend analysis, customer preferences and competitive analysis.
## Link to **dataset**
[Italy - Saint Laurent - Fashion Standard Categories dataset](https://www.databoutique.com/buy-data-page/Saint%20Laurent%20Standard%20Categories%20Italy/r/recFv379kR9jEGWhK)
|
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DBQ/Farfetch.Product.prices.Singapore
|
DBQ
| 2023-11-19T09:12:29Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Farfetch",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:11:06Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Singapore - Farfetch - Product-level price list
tags:
- webscraping
- ecommerce
- Farfetch
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 225892320
num_examples: 602976
download_size: 80912778
dataset_size: 225892320
---
# Farfetch web scraped data
## About the website
The **Ecommerce industry** in the Asia Pacific, particularly in **Singapore**, has experienced significant growth in recent years, largely attributed to the rapid digital transformation and the increasing internet penetration rate. The so-called "Lion City" has become a hub for technological advancements and digital innovations. Companies like **Farfetch** have taken advantage of this and established a solid presence, offering a myriad of luxury fashion products online. The dataset studied comprises **Ecommerce product-list page (PLP) data** on Farfetch in Singapore, demonstrating the reach and impact of such platforms in the blossoming regional market.
## Link to **dataset**
[Singapore - Farfetch - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Farfetch%20Product-prices%20Singapore/r/recZTFqL4hIx7jJnk)
|
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DBQ/Fendi.Product.prices.Germany
|
DBQ
| 2023-11-19T09:12:43Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Fendi",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:12:38Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Germany - Fendi - Product-level price list
tags:
- webscraping
- ecommerce
- Fendi
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 823658
num_examples: 1992
download_size: 255541
dataset_size: 823658
---
# Fendi web scraped data
## About the website
The **fashion industry** in EMEA, particularly **Germany**, has gradually incorporated the growth and importance of the **digital marketplace**. High-end retailers, such as Fendi, are now focusing on providing luxury shopping experiences online which imitate the opulence of their physical stores, encompassing the entire purchase journey from product discovery to delivery. The dataset observed includes **Ecommerce product-list page (PLP) data** specifically on **Fendi in Germany**. This reflects the tailored digital consumer experiences in the significant shift to **online luxury retail**. Germany is setting the regional trend with their strong underlying ecommerce infrastructure and digital shopping habits.
## Link to **dataset**
[Germany - Fendi - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Fendi%20Product-prices%20Germany/r/recOaqMZaRUVnLhzZ)
|
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DBQ/Mr.Porter.Product.prices.Russia
|
DBQ
| 2023-11-19T09:12:59Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:12:53Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Russia - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 8722278
num_examples: 26612
download_size: 2022635
dataset_size: 8722278
---
# Mr Porter web scraped data
## About the website
**Mr Porter** is a prominent operator in the **online retail industry** within the EMEA region, specifically in **Russia**. The E-commerce industry in Russia is growing rapidly, amidst the increasing tech-savviness and online shopping habits of consumers. Mr Porters foundation on sophisticated technology offers them a competitive edge, especially in tailoring to Russias vast and diverse consumer base. **E-commerce**, particularly **online fashion retailing**, is emerging as a dominant industry, influenced by factors like advanced technology, easy access to the internet, and changing consumer patterns. The dataset under observation contains **Ecommerce product-list page (PLP) data** pertaining to Mr Porters operations in Russia.
## Link to **dataset**
[Russia - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Russia/r/reckLATEvXV9HEd9d)
|
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DBQ/Mr.Porter.Product.prices.United.States
|
DBQ
| 2023-11-19T09:13:14Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:13:08Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United States - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 11724890
num_examples: 35741
download_size: 2718002
dataset_size: 11724890
---
# Mr Porter web scraped data
## About the website
Mr Porter operates within the **e-commerce industry**, specifically within the **mens luxury fashion** segment, in the United States. This industry has consistently demonstrated strong growth throughout the Americas, particularly in the United States where online retail is booming. The ability to purchase high-end fashion items online has revolutionized how American consumers shop, making upscale fashion more accessible. This specific dataset provides **Ecommerce product-list page (PLP) data** on Mr Porter, which serves as a key insight into customer preferences, popular items, and overall market trends in the United States. Digital marketing techniques have strengthened this shopping medium, making data collection easier and more effective, thereby boosting this sectors potential for profitability.
## Link to **dataset**
[United States - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20United%20States/r/recPOcpE0GqT6uLVc)
|
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DBQ/Loro.Piana.Product.prices.Japan
|
DBQ
| 2023-11-19T09:13:26Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Loro Piana",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:13:22Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Japan - Loro Piana - Product-level price list
tags:
- webscraping
- ecommerce
- Loro Piana
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 238989
num_examples: 685
download_size: 80375
dataset_size: 238989
---
# Loro Piana web scraped data
## About the website
Loro Piana operates within the **luxury fashion industry** in the **Asia Pacific** region, particularly **Japan**. This industry is noted for its high-end products and services, catering to a demographic that values exclusivity, quality, and prestige. Japan is a major market, with a sophisticated consumer base that posseses a keen interest in luxury fashion. The **ecommerce sector** in this region has seen substantial growth, with digitization and connectedness driving more consumers online for their luxury purchases. The dataset observed includes **Ecommerce product-list page (PLP) data** on **Loro Piana** in Japan, thus giving insight into the brands online presence and performance in this lucrative market.
## Link to **dataset**
[Japan - Loro Piana - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Loro%20Piana%20Product-prices%20Japan/r/recdgxSHfd3wfZDQO)
|
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DBQ/Fendi.Product.prices.Canada
|
DBQ
| 2023-11-19T09:13:39Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Fendi",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:13:34Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Canada - Fendi - Product-level price list
tags:
- webscraping
- ecommerce
- Fendi
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 642904
num_examples: 1610
download_size: 207514
dataset_size: 642904
---
# Fendi web scraped data
## About the website
The **luxury fashion industry** is a vibrant and thriving sector in the Americas, particularly in **Canada**. This industry is marked by the sale of high-end apparel, accessories, footwear, jewelry, and beauty products from renowned global brands like **Fendi**. Moreover, Canadas luxury fashion industry has embraced digital transformation, leading to a significant growth of **Ecommerce** platforms. Flourishing online sales have proven to be a potent driver for the sectors advancements. A recent dataset analysis provides a comprehensive overlook of **Ecommerce product-list page (PLP) data on Fendi** in Canada, illustrating the brands online presence and its market performance against the backdrop of the countrys digital shopping trend.
## Link to **dataset**
[Canada - Fendi - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Fendi%20Product-prices%20Canada/r/recZKFVGijTds1hUc)
|
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] |
DBQ/Gucci.Product.prices.Canada
|
DBQ
| 2023-11-19T09:13:53Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:13:48Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Canada - Gucci - Product-level price list
tags:
- webscraping
- ecommerce
- Gucci
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 2475271
num_examples: 5185
download_size: 750587
dataset_size: 2475271
---
# Gucci web scraped data
## About the website
Gucci is a key player in the **luxury fashion industry** in the Americas, particularly in **Canada**. This industry encompasses a broad range of high-end, premium products such as clothing, accessories, fragrances, and watches, which are regarded for their exceptional quality and exclusivity. The brand has strongly maintained its demand and popularity through both its brick-and-mortar shops and its **e-commerce presence**. The dataset observed includes **Ecommerce product-list page (PLP) data on Gucci** in Canada, indicating online shopping trends and consumer behaviors in relation to Guccis products in the digital marketplace.
## Link to **dataset**
[Canada - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Canada/r/recwapX0etBkgXR6t)
|
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DBQ/Prada.Product.prices.Portugal
|
DBQ
| 2023-11-19T09:14:05Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Prada",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:14:00Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Portugal - Prada - Product-level price list
tags:
- webscraping
- ecommerce
- Prada
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1285620
num_examples: 2548
download_size: 385056
dataset_size: 1285620
---
# Prada web scraped data
## About the website
The **luxury fashion industry** in the **EMEA** (Europe, Middle East, and Africa) region is a robust and highly dynamic sector, with particular emphasis on the thriving market in **Portugal**. Best known for their high-end labels, this industry is marked by factors such as premium quality, design aesthetics, and brand reputation. **Prada**, a renowned Italian luxury fashion house, holds a significant place in this industry. The industry is fast adapting to the digital era, with notable progress in the realm of **Ecommerce**. As per the observed dataset, we have **product-list page (PLP)** data pertaining to Pradas performance in the Portugal market. The data is reflective of Prada’s online presence and Ecommerce strategies in the region.
## Link to **dataset**
[Portugal - Prada - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Prada%20Product-prices%20Portugal/r/recWitpZbmrikZDl1)
|
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DBQ/Net.a.Porter.Product.prices.United.Kingdom
|
DBQ
| 2023-11-19T09:14:32Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Net",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:14:23Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United Kingdom - Net-a-Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Net
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 23194278
num_examples: 56980
download_size: 6865739
dataset_size: 23194278
---
# Net-a-Porter web scraped data
## About the website
Net-a-Porter operates in the highly competitive **e-commerce industry** in EMEA, particularly in the **United Kingdom**. This vibrant industry is characterized by a dynamic digital environment encompassing various segments, notably **fashion**, **luxury goods**, and **retail**. These segments continue to witness significant growth, driven by advancements in technology and changing consumer behaviors. Net-a-Porter, being a premium online luxury fashion retail platform, significantly contributes to the e-commerce landscape. The dataset observed includes **Ecommerce product-list page (PLP) data on Net-a-Porter** in the United Kingdom, offering insightful information about the sites performance, trending products, consumer preferences, and much more.
## Link to **dataset**
[United Kingdom - Net-a-Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Net-a-Porter%20Product-prices%20United%20Kingdom/r/recBUdYFZQj4tUnIX)
|
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DBQ/Blickers.Product.prices.United.Kingdom
|
DBQ
| 2023-11-19T09:14:50Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Blickers",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:14:42Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United Kingdom - Blickers - Product-level price list
tags:
- webscraping
- ecommerce
- Blickers
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 10697404
num_examples: 28256
download_size: 5228722
dataset_size: 10697404
---
# Blickers web scraped data
## About the website
The Blickers operates in the **eCommerce industry** in the EMEA region, predominantly in the **United Kingdom**. This industrys key essence revolves around buying and selling goods or services using the internet and transferring money and data to carry out transactions. It has dramatically redefined many consumers shopping habits in the UK. The dataset observed involves **eCommerce product-list page (PLP) data** for Blickers operations in the UK. The dataset specifically provides comprehensive insights into shoppers online behavior, preferences, and purchase trends, all critical aspects of understanding the ever-evolving **eCommerce landscape in the United Kingdom** and beyond.
## Link to **dataset**
[United Kingdom - Blickers - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Blickers%20Product-prices%20United%20Kingdom/r/rec5q13pQBDDUAHZT)
|
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DBQ/Bottega.Veneta.Product.prices.United.States
|
DBQ
| 2023-11-19T09:15:04Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Bottega Veneta",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:14:59Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United States - Bottega Veneta - Product-level price list
tags:
- webscraping
- ecommerce
- Bottega Veneta
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1614857
num_examples: 4469
download_size: 452053
dataset_size: 1614857
---
# Bottega Veneta web scraped data
## About the website
Bottega Veneta is a prominent player in the **Luxury Fashion** industry in the **Americas**, particularly in the **United States**. This industry is defined by high-end clothing, accessories, leather goods, shoes and lifestyle items from distinctive brands. In the US, the Luxury Fashion market is shaped by trends like digitalization, personalized experiences, and sustainability. A significant amount of retail activity occurs on digital platforms. The dataset observed presents **Ecommerce product-list page (PLP)** data on **Bottega Veneta** in the United States, highlighting the brands online retail presence in the industry. Ecommerce has become increasingly important in the Luxury Fashion sector as a direct-to-consumer avenue.
## Link to **dataset**
[United States - Bottega Veneta - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Bottega%20Veneta%20Product-prices%20United%20States/r/recZ249aYzkZQZLPx)
|
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DBQ/Gucci.Product.prices.Australia
|
DBQ
| 2023-11-19T09:15:20Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Gucci",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:15:15Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Australia - Gucci - Product-level price list
tags:
- webscraping
- ecommerce
- Gucci
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 1407422
num_examples: 2972
download_size: 415524
dataset_size: 1407422
---
# Gucci web scraped data
## About the website
The **luxury fashion industry** in the **Asia Pacific** region, specifically in **Australia**, continues to flourish owing to its affluent consumer base and expanding online presence. Brands like **Gucci** have a substantial footprint in this marketplace. In Australia, luxury retailing has seen impressive growth, with luxury fashion houses progressively increasing their digital presence to meet the changing consumer behavior. The availability of **Ecommerce product-list page (PLP) data** on Guccis Australian market provides valuable insights into the fashion houses performance and the shopping behavior of its clientele. This data is instrumental in examining market trends, consumer preference, and buying patterns in the **Australian luxury fashion market**.
## Link to **dataset**
[Australia - Gucci - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Gucci%20Product-prices%20Australia/r/recoAQnDLFcGoAEIN)
|
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] |
DBQ/Burberry.Product.prices.United.Arab.Emirates
|
DBQ
| 2023-11-19T09:15:32Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Burberry",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:15:28Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: United Arab Emirates - Burberry - Product-level price list
tags:
- webscraping
- ecommerce
- Burberry
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 809175
num_examples: 2554
download_size: 234729
dataset_size: 809175
---
# Burberry web scraped data
## About the website
The **luxury fashion industry** in the **EMEA region**, particularly in the **United Arab Emirates**, is a robust and rapidly growing market. This growth is primarily fuelled by the affluent consumer base and tourists, who have a keen interest in high-end and prestigious fashion labels. Middle Eastern consumers often look at luxury goods as a status symbol, thus driving up demand for premium brands like **Burberry**. In recent years, **Ecommerce** in this sector has seen significant growth, with online platforms becoming a major sales channel. The dataset observed provides in-depth analysis of **Ecommerce product-list page (PLP) data** on Burberry in the United Arab Emirates.
## Link to **dataset**
[United Arab Emirates - Burberry - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Burberry%20Product-prices%20United%20Arab%20Emirates/r/recg0ZpPS4sh1eFAU)
|
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DBQ/Mr.Porter.Product.prices.Sweden
|
DBQ
| 2023-11-19T09:15:48Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:15:43Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Sweden - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 9100964
num_examples: 27695
download_size: 2082496
dataset_size: 9100964
---
# Mr Porter web scraped data
## About the website
The **EMEA region**, specifically **Sweden**, has seen a significant rise in the **luxury online retail industry**, where **Mr Porter** operates. The growth has primarily been driven by the fast-paced digitalization, significant internet penetration, and a growing number of digitally native consumers. Additionally, Swedish consumers, renowned for their fashion-forward approach, have demonstrated a strong appetite for luxury fashion products, favouring convenience, choice, and quality offered by online retailers like Mr Porter. The dataset in question provides comprehensive insight into **Ecommerce product-list page (PLP) data** on Mr Porters operation in Sweden, unveiling valuable customer trends and product preferences.
## Link to **dataset**
[Sweden - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20Sweden/r/recKNdRjHlSCpbHYz)
|
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DBQ/Louis.Vuitton.Product.prices.Canada
|
DBQ
| 2023-11-19T09:16:02Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Louis Vuitton",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:15:57Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: Canada - Louis Vuitton - Product-level price list
tags:
- webscraping
- ecommerce
- Louis Vuitton
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 3461232
num_examples: 8151
download_size: 912831
dataset_size: 3461232
---
# Louis Vuitton web scraped data
## About the website
The **luxury fashion industry** in the Americas, specifically in **Canada** is flourishing and significantly competitive. A vital player, **Louis Vuitton**, has crucially attained a strong positioning in this market. The industry in focus encompasses high-end, exclusive products and services, which are in high demand amongst the affluent sections of society. These products typically include haute couture, ready-to-wear clothing, handbags, perfumes, and accessories, amongst other items. The industry is primarily based in fashion capitals like New York, but it has a vast and significant reach across the entire region. The dataset observed provides valuable insights from an **Ecommerce product-list page (PLP)** specifically for Louis Vuittons operations in Canada.
## Link to **dataset**
[Canada - Louis Vuitton - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Louis%20Vuitton%20Product-prices%20Canada/r/recj2WoaJ5aLp1fxA)
|
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] |
DBQ/Chloe.Product.prices.South.Korea
|
DBQ
| 2023-11-19T09:16:16Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Chloe",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:16:11Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: South Korea - Chloe - Product-level price list
tags:
- webscraping
- ecommerce
- Chloe
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: string
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 677692
num_examples: 2439
download_size: 158885
dataset_size: 677692
---
# Chloe web scraped data
## About the website
The **Ecommerce industry** in the **Asia Pacific region**, particularly in **South Korea**, is experiencing remarkable growth. Fueling this rapid expansion are tech-savvy consumers who are rapidly adopting online shopping with ease and comfort. Companies like **Chloe** are leveraging the popularity of Ecommerce to extend their market reach. The dataset currently under observation contains **Ecommerce product-list page (PLP) data** specifically concerning Chloe in South Korea. This critical data offers deep insights into consumer purchasing habits, shifts in market trends, and performance analysis, thereby fostering a wealth of opportunities for Chloe to further expand its online presence.
## Link to **dataset**
[South Korea - Chloe - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Chloe%20Product-prices%20South%20Korea/r/recVfyRPRYg697Wup)
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DBQ/Mr.Porter.Product.prices.South.Korea
|
DBQ
| 2023-11-19T09:16:30Z | 0 | 0 |
[
"task_categories:text-classification",
"task_categories:image-classification",
"task_categories:feature-extraction",
"task_categories:image-segmentation",
"task_categories:image-to-image",
"task_categories:image-to-text",
"task_categories:object-detection",
"task_categories:summarization",
"task_categories:zero-shot-image-classification",
"annotations_creators:other",
"language_creators:other",
"multilinguality:monolingual",
"source_datasets:original",
"language:en",
"license:unknown",
"webscraping",
"ecommerce",
"Mr Porter",
"fashion",
"fashion product",
"image",
"fashion image",
"region:us"
] |
[
"text-classification",
"image-classification",
"feature-extraction",
"image-segmentation",
"image-to-image",
"image-to-text",
"object-detection",
"summarization",
"zero-shot-image-classification"
] | 2023-11-19T09:16:25Z |
---
annotations_creators:
- other
language_creators:
- other
language:
- en
license:
- unknown
multilinguality:
- monolingual
source_datasets:
- original
task_categories:
- text-classification
- image-classification
- feature-extraction
- image-segmentation
- image-to-image
- image-to-text
- object-detection
- summarization
- zero-shot-image-classification
pretty_name: South Korea - Mr Porter - Product-level price list
tags:
- webscraping
- ecommerce
- Mr Porter
- fashion
- fashion product
- image
- fashion image
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: website_name
dtype: string
- name: competence_date
dtype: string
- name: country_code
dtype: string
- name: currency_code
dtype: string
- name: brand
dtype: string
- name: category1_code
dtype: string
- name: category2_code
dtype: string
- name: category3_code
dtype: string
- name: product_code
dtype: int64
- name: title
dtype: string
- name: itemurl
dtype: string
- name: imageurl
dtype: string
- name: full_price
dtype: float64
- name: price
dtype: float64
- name: full_price_eur
dtype: float64
- name: price_eur
dtype: float64
- name: flg_discount
dtype: int64
splits:
- name: train
num_bytes: 8881255
num_examples: 27053
download_size: 2126775
dataset_size: 8881255
---
# Mr Porter web scraped data
## About the website
The **Ecommerce Industry** is booming significantly in the Asia Pacific region, notably so in **South Korea**. In part, this market growth is driven by the increasing use of the internet and smartphone devices which further facilitate online shopping. One of the notable players operating in this industry is **Mr Porter**, a renowned international mens luxury wear online retailer. The dataset obtained for analysis contains **Ecommerce product-list page (PLP) data** of **Mr Porter** in South Korea. This data will offer insightful details about online customer interactions, preferences, and trends in the South Korean luxury retail market.
## Link to **dataset**
[South Korea - Mr Porter - Product-level price list dataset](https://www.databoutique.com/buy-data-page/Mr%20Porter%20Product-prices%20South%20Korea/r/recmSQCD6UD9QZEqq)
|
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] |
Elfsong/Bias_NLI
|
Elfsong
| 2023-11-19T10:43:47Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T10:37:12Z |
---
dataset_info:
features:
- name: premise
dtype: string
- name: hypothesis
dtype: string
- name: label
dtype:
class_label:
names:
'0': entailment
'1': neutral
'2': contradiction
splits:
- name: train
num_bytes: 136716914
num_examples: 1912390
download_size: 48712225
dataset_size: 136716914
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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Vaibhav78/car
|
Vaibhav78
| 2023-11-19T11:13:09Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T11:13:09Z |
---
license: openrail
---
|
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] |
La-matrice/french_sentences_19M
|
La-matrice
| 2023-11-28T07:15:04Z | 0 | 0 |
[
"task_categories:text-generation",
"language:fr",
"region:us"
] |
[
"text-generation"
] | 2023-11-19T11:38:49Z |
---
task_categories:
- text-generation
language:
- fr
pretty_name: f
---
## This dataset consists of more than 19 million French sentences.
This diverse collection originates from a variety of sources, including books, songs, Wikipedia, and translation datasets.
|
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wbxlala/Dreamer_Valence_shuffled
|
wbxlala
| 2023-11-19T11:44:09Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T11:43:56Z |
---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: float64
- name: label
dtype: float64
splits:
- name: train
num_bytes: 499671504.0
num_examples: 414
download_size: 492782677
dataset_size: 499671504.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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wbxlala/Dreamer_Dominance_shuffled
|
wbxlala
| 2023-11-19T11:44:54Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T11:44:35Z |
---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: float64
- name: label
dtype: float64
splits:
- name: train
num_bytes: 499671504.0
num_examples: 414
download_size: 492768642
dataset_size: 499671504.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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peldrak/riviera_labeled_test
|
peldrak
| 2023-11-23T13:11:56Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T11:49:10Z |
---
dataset_info:
features:
- name: pixel_values
dtype: image
- name: label
dtype: image
splits:
- name: train
num_bytes: 298529487.0
num_examples: 231
download_size: 67692943
dataset_size: 298529487.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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CJWeiss/multilong_id_rename
|
CJWeiss
| 2023-11-19T12:13:40Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T12:12:58Z |
---
dataset_info:
features:
- name: id
dtype: int64
- name: input
dtype: string
- name: output
dtype: string
splits:
- name: train
num_bytes: 1376297066
num_examples: 3404
- name: test
num_bytes: 260869872
num_examples: 682
- name: valid
num_bytes: 206485006
num_examples: 453
download_size: 833197169
dataset_size: 1843651944
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
- split: valid
path: data/valid-*
---
# Dataset Card for "multilong_id_rename"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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wbxlala/Dreamer_Valence_sub1
|
wbxlala
| 2023-11-19T12:28:07Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T12:28:05Z |
---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: float64
- name: label
dtype: float64
splits:
- name: train
num_bytes: 21724848
num_examples: 18
download_size: 21683447
dataset_size: 21724848
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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wbxlala/Dreamer_Dominance_sub1
|
wbxlala
| 2023-11-19T12:31:34Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T12:31:32Z |
---
dataset_info:
features:
- name: image
sequence:
sequence:
sequence: float64
- name: label
dtype: float64
splits:
- name: train
num_bytes: 21724848
num_examples: 18
download_size: 21683446
dataset_size: 21724848
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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CJWeiss/lcr_final_id_rename
|
CJWeiss
| 2023-11-19T12:54:09Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T12:53:59Z |
---
dataset_info:
features:
- name: input
dtype: string
- name: output
dtype: string
- name: id
dtype: int64
splits:
- name: train
num_bytes: 82132163
num_examples: 2918
- name: test
num_bytes: 18921115
num_examples: 584
- name: valid
num_bytes: 12959086
num_examples: 389
download_size: 56066938
dataset_size: 114012364
---
# Dataset Card for "lcr_final_id_rename"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
NobodyExistsOnTheInternet/unchat-vs-chat-results
|
NobodyExistsOnTheInternet
| 2023-11-19T13:02:40Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-19T13:00:15Z |
---
license: mit
---
This is the chat vs unchat comparision for ToxicQA
Base model used is llama-2-chat-7b
|
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Bartolomeupaiva20/Eudes_carvalho_modelo_de-voz
|
Bartolomeupaiva20
| 2023-11-19T13:11:35Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T13:01:23Z |
---
license: openrail
---
|
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] |
CortexLM/dalle-3-dataset
|
CortexLM
| 2023-11-20T13:42:27Z | 0 | 0 |
[
"task_categories:text-to-image",
"language:en",
"license:unknown",
"region:us"
] |
[
"text-to-image"
] | 2023-11-19T13:27:44Z |
---
license: unknown
task_categories:
- text-to-image
language:
- en
---
# **DALL·E 3 Dataset by Bittensor Network (NetUID 18)**
**Description :** This dataset was generated by Subnetwork 18 (Bittensor), utilizing the capabilities of DALL·E 3.
**WanDB :** [Cortex-T Wandb](https://wandb.ai/cortex-t/synthetic-QA/)
**Disclaimer: Image Attribution and Copyright Notice**
The images included in this dataset have been sourced from WandB (Weights and Biases). While every effort has been made to ensure compliance with copyright and intellectual property rights, Cortex Foundation cannot guarantee the absence of any copyright or intellectual property infringements.
Cortex Foundation assumes no responsibility or liability for any potential copyright issues associated with the images in this dataset. Users of this dataset are strongly encouraged to verify the copyright status of individual images and ensure compliance with applicable laws and regulations before using or redistributing the dataset.
By accessing and using this dataset, you acknowledge and agree that Cortex Foundation is not responsible for any copyright violations or legal consequences that may arise from the use of these images.
If you have any concerns or questions regarding the copyright status of specific images, please contact the original source or copyright holder directly.
Cortex Foundation reserves the right to update or modify this disclaimer as needed to reflect any changes in the dataset's composition or to address emerging legal or ethical considerations.
|
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] |
carcanha/marilha
|
carcanha
| 2023-11-19T13:34:47Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T13:29:48Z |
---
license: openrail
---
|
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ludekcizinsky/epfl-cs502-hw3
|
ludekcizinsky
| 2023-11-19T13:36:55Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-19T13:31:37Z |
---
license: mit
---
|
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huggingfacejs/tasks
|
huggingfacejs
| 2023-11-19T13:35:31Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-19T13:33:11Z |
---
license: mit
---
This dataset is for storing assets for https://huggingface.co/tasks and https://github.com/huggingface/huggingface.js/tree/main/packages/tasks
|
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] |
CortexLM/dalle-2-dataset
|
CortexLM
| 2023-11-19T13:42:34Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-19T13:42:34Z |
---
license: unknown
---
|
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Robin246/inxai_dataset
|
Robin246
| 2023-11-19T13:56:52Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T13:56:46Z |
---
dataset_info:
features:
- name: action_needed
dtype: int64
- name: action
dtype: string
- name: category
dtype: string
- name: input_sentence
dtype: string
- name: reply
dtype: string
splits:
- name: train
num_bytes: 53802
num_examples: 533
download_size: 19323
dataset_size: 53802
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# Dataset Card for "inxai_dataset"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
CortexLM/gpt-4-dataset
|
CortexLM
| 2023-11-19T13:59:22Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-19T13:59:22Z |
---
license: unknown
---
|
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CortexLM/gpt-3.5-dataset
|
CortexLM
| 2023-11-19T13:59:41Z | 0 | 0 |
[
"license:unknown",
"region:us"
] | null | 2023-11-19T13:59:41Z |
---
license: unknown
---
|
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] |
shunk031/PosterErase
|
shunk031
| 2023-11-19T14:43:14Z | 0 | 0 |
[
"task_categories:other",
"annotations_creators:machine-generated",
"language_creators:found",
"multilinguality:monolingual",
"source_datasets:original",
"language:zh",
"license:cc-by-sa-4.0",
"graphic design",
"arxiv:2204.12743",
"region:us"
] |
[
"other"
] | 2023-11-19T14:42:04Z |
---
annotations_creators:
- machine-generated
language:
- zh
language_creators:
- found
license:
- cc-by-sa-4.0
multilinguality:
- monolingual
pretty_name: PosterErase
size_categories: []
source_datasets:
- original
tags:
- graphic design
task_categories:
- other
task_ids: []
---
# Dataset Card for PosterErase
[](https://github.com/shunk031/huggingface-datasets_PosterErase/actions/workflows/ci.yaml)
## Table of Contents
- [Dataset Card Creation Guide](#dataset-card-creation-guide)
- [Table of Contents](#table-of-contents)
- [Dataset Description](#dataset-description)
- [Dataset Summary](#dataset-summary)
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
- [Languages](#languages)
- [Dataset Structure](#dataset-structure)
- [Data Instances](#data-instances)
- [Data Fields](#data-fields)
- [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
- [Curation Rationale](#curation-rationale)
- [Source Data](#source-data)
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
- [Who are the source language producers?](#who-are-the-source-language-producers)
- [Annotations](#annotations)
- [Annotation process](#annotation-process)
- [Who are the annotators?](#who-are-the-annotators)
- [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
- [Social Impact of Dataset](#social-impact-of-dataset)
- [Discussion of Biases](#discussion-of-biases)
- [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
- [Dataset Curators](#dataset-curators)
- [Licensing Information](#licensing-information)
- [Citation Information](#citation-information)
- [Contributions](#contributions)
## Dataset Description
- **Homepage:** https://github.com/alimama-creative/Self-supervised-Text-Erasing
- **Repository:** https://github.com/shunk031/huggingface-datasets_PosterErase
- **Paper (Preprint):** https://arxiv.org/abs/2204.12743
- **Paper (ACMMM2022):** https://dl.acm.org/doi/abs/10.1145/3503161.3547905
### Dataset Summary
### Supported Tasks and Leaderboards
[More Information Needed]
### Languages
The language data in PKU-PosterLayout is in Chinese (BCP-47 zh).
## Dataset Structure
### Data Instances
To use PosterErase dataset, you need to download the dataset via [Alibaba Cloud](https://tianchi.aliyun.com/dataset/134810).
Then place the downloaded files in the following structure and specify its path.
```
/path/to/datasets
├── erase_1.zip
├── erase_2.zip
├── erase_3.zip
├── erase_4.zip
├── erase_5.zip
└── erase_6.zip
```
```python
import datasets as ds
dataset = ds.load_dataset(
path="shunk031/PosterErase",
data_dir="/path/to/datasets/",
)
```
### Data Fields
[More Information Needed]
### Data Splits
[More Information Needed]
## Dataset Creation
### Curation Rationale
[More Information Needed]
### Source Data
[More Information Needed]
#### Initial Data Collection and Normalization
[More Information Needed]
#### Who are the source language producers?
[More Information Needed]
### Annotations
[More Information Needed]
#### Annotation process
[More Information Needed]
#### Who are the annotators?
[More Information Needed]
### Personal and Sensitive Information
[More Information Needed]
## Considerations for Using the Data
### Social Impact of Dataset
[More Information Needed]
### Discussion of Biases
[More Information Needed]
### Other Known Limitations
[More Information Needed]
## Additional Information
### Dataset Curators
[More Information Needed]
### Licensing Information
You can find the following statement in [the license section](https://tianchi.aliyun.com/dataset/134810#license) of t[he dataset distribution location](https://tianchi.aliyun.com/dataset/134810).
> The dataset is distributed under the CC BY-SA 4.0 license.
However, the license setting on that page appears to be set to [CC-BY-SA-NC 4.0](http://creativecommons.org/licenses/by-sa/4.0/?spm=a2c22.12282016.0.0.7abc5a92qnyxdR).
### Citation Information
```bibtex
@inproceedings{jiang2022self,
title={Self-supervised text erasing with controllable image synthesis},
author={Jiang, Gangwei and Wang, Shiyao and Ge, Tiezheng and Jiang, Yuning and Wei, Ying and Lian, Defu},
booktitle={Proceedings of the 30th ACM International Conference on Multimedia},
pages={1973--1983},
year={2022}
}
```
### Contributions
Thanks to [alimama-creative](https://github.com/alimama-creative) for creating this dataset.
|
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] |
carcanha/marilhamendonsa
|
carcanha
| 2023-11-19T14:55:55Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T14:54:06Z |
---
license: openrail
---
|
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] |
vaitom/toynow
|
vaitom
| 2023-11-21T03:12:20Z | 0 | 0 |
[
"license:cc",
"region:us"
] | null | 2023-11-19T15:01:03Z |
---
license: cc
---
|
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] |
titanite09/SkinDiseaseDataset
|
titanite09
| 2023-11-19T15:07:27Z | 0 | 0 |
[
"task_categories:image-classification",
"size_categories:10K<n<100K",
"language:en",
"license:apache-2.0",
"medical",
"region:us"
] |
[
"image-classification"
] | 2023-11-19T15:06:21Z |
---
license: apache-2.0
task_categories:
- image-classification
language:
- en
tags:
- medical
size_categories:
- 10K<n<100K
---
|
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ihaflix1/vozdocumentario
|
ihaflix1
| 2023-11-19T15:47:32Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T15:46:25Z |
---
license: openrail
---
|
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] |
anarubioruiz/ARIA-PaLM-text
|
anarubioruiz
| 2023-11-19T16:06:56Z | 0 | 0 |
[
"task_categories:text-generation",
"size_categories:n<1K",
"language:en",
"license:apache-2.0",
"region:us"
] |
[
"text-generation"
] | 2023-11-19T16:02:31Z |
---
license: apache-2.0
task_categories:
- text-generation
language:
- en
size_categories:
- n<1K
---
|
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chemNLP/clinical-trials-v2
|
chemNLP
| 2023-11-20T18:27:13Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T16:17:14Z |
---
dataset_info:
features:
- name: filename
dtype: string
- name: xml
dtype: string
- name: text
dtype: string
splits:
- name: train
num_bytes: 12800535347
num_examples: 456224
download_size: 3738991719
dataset_size: 12800535347
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
|
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] |
SchubergPhilis/OpenAssistant-Top1-ENG-V1
|
SchubergPhilis
| 2023-11-19T17:00:01Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-19T16:49:43Z |
---
license: apache-2.0
---
### OpenAssistant Conversations Dataset (OASST1-TOP1) English
#### Dataset Summary
This is an OpenAssistant Coversations dataset TOP1, only in English language only. The dataset contains 4253 conversations. It is a test dataset, aimed to reduce the fine tuning time for English models used in a RAG system.
For furthur information and data structure, please refer to the OpenAssistant page:
https://huggingface.co/datasets/OpenAssistant/oasst1
|
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BangumiBase/shikkakumonnosaikyoukenja
|
BangumiBase
| 2023-11-19T18:47:53Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-19T17:00:50Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Shikkaku Mon No Saikyou Kenja
This is the image base of bangumi Shikkaku Mon no Saikyou Kenja, we detected 35 characters, 2876 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 893 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 9 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 19 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 99 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 16 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 27 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 19 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 31 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 22 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 86 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 14 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 22 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 13 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 8 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 19 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 31 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 10 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 9 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 15 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 10 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 9 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 13 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 293 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 11 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 27 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 469 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 23 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 7 | [Download](27/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 28 | 6 | [Download](28/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 29 | 12 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 467 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 8 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 6 | [Download](32/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 33 | 6 | [Download](33/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| noise | 147 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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] |
bnasderf/puronomo
|
bnasderf
| 2023-11-19T17:31:08Z | 0 | 0 |
[
"license:bigcode-openrail-m",
"region:us"
] | null | 2023-11-19T17:29:44Z |
---
license: bigcode-openrail-m
---
|
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] |
marcos292/LuizCarvalho
|
marcos292
| 2023-11-19T17:40:27Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T17:38:46Z |
---
license: openrail
---
|
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cointegrated/taiga_stripped_rest
|
cointegrated
| 2023-11-23T09:48:58Z | 0 | 0 |
[
"task_categories:text-generation",
"task_categories:fill-mask",
"size_categories:1M<n<10M",
"language:ru",
"license:cc-by-sa-3.0",
"taiga",
"tayga",
"region:us"
] |
[
"text-generation",
"fill-mask"
] | 2023-11-19T17:56:03Z |
---
dataset_info:
features:
- name: text
dtype: string
- name: file
dtype: string
splits:
- name: Arzamas
num_bytes: 4725465
num_examples: 311
- name: Interfax
num_bytes: 82478694
num_examples: 46000
- name: Lenta
num_bytes: 99984639
num_examples: 36000
- name: Magazines
num_bytes: 2295653294
num_examples: 39000
- name: NPlus1
num_bytes: 23506941
num_examples: 7000
- name: KP
num_bytes: 65444392
num_examples: 45000
- name: Fontanka
num_bytes: 840679591
num_examples: 342683
- name: Subtitles
num_bytes: 311508573
num_examples: 7903
- name: social
num_bytes: 600396164
num_examples: 804356
download_size: 2180717682
dataset_size: 4324377753
license: cc-by-sa-3.0
language:
- ru
tags:
- taiga
- tayga
size_categories:
- 1M<n<10M
task_categories:
- text-generation
- fill-mask
---
# Dataset Card for "taiga_stripped_rest"
This is a subset of the Taiga corpus (https://tatianashavrina.github.io/taiga_site), derived from the all the sources, except
[stihi](https://huggingface.co/datasets/cointegrated/taiga_stripped_stihi) and [proza](https://huggingface.co/datasets/cointegrated/taiga_stripped_proza):
`Arzamas`, `Interfax`, `Lenta`, `Magazines`, `NPlus1`, `KP`, `Fontanka`, `Subtitles` and `social`.
The dataset consists of plain texts, without morphological and syntactic annotation or metainformation.
For the `Subtitles` subset, we dropped all non-Russian texts.
For the `social` subset, we split the texts into indidividual database items,
or (for LiveJournal) into "posts" (defined as lines with 1000+ characters) and subsequent "comments".
For more details and analysis, and for the texts with annotation or metadata, please refer to website of the corpus.
Other subsets of Taiga: [proza](https://huggingface.co/datasets/cointegrated/taiga_stripped_proza) (fiction)
and [stihi](https://huggingface.co/datasets/cointegrated/taiga_stripped_stihi) (poetry).
License: [CC BY-SA 3.0](https://creativecommons.org/licenses/by-sa/3.0/).
|
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gilsonk12/gilson22
|
gilsonk12
| 2023-11-19T18:08:57Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T18:00:16Z |
---
license: openrail
---
|
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] |
Praghxx/Bryann
|
Praghxx
| 2023-11-19T18:24:28Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T18:23:53Z |
---
license: openrail
---
|
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] |
aucelio/pedro
|
aucelio
| 2023-11-19T18:54:51Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T18:51:19Z |
---
license: openrail
---
|
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BangumiBase/rakudaikishinocavalry
|
BangumiBase
| 2023-11-19T20:05:25Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-19T18:55:29Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Rakudai Kishi No Cavalry
This is the image base of bangumi Rakudai Kishi no Cavalry, we detected 20 characters, 1314 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 305 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 19 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 365 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 41 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 62 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 44 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 47 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 23 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 105 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 9 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 18 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 20 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 9 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 10 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 9 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 9 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 37 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 6 | [Download](17/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 18 | 19 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 157 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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BangumiBase/masougakuenhxh
|
BangumiBase
| 2023-11-19T20:20:49Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-19T19:04:51Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Masou Gakuen Hxh
This is the image base of bangumi Masou Gakuen HxH, we detected 22 characters, 1642 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 183 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 62 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 55 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 160 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 21 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 80 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 488 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 32 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 12 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 5 | [Download](9/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 10 | 80 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 68 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 24 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 32 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 33 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 16 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 38 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 20 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 6 | [Download](18/dataset.zip) |  |  |  |  |  |  | N/A | N/A |
| 19 | 67 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 7 | [Download](20/dataset.zip) |  |  |  |  |  |  |  | N/A |
| noise | 153 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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kelen0102/Caine
|
kelen0102
| 2023-11-19T19:08:25Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T19:06:53Z |
---
license: openrail
---
|
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nxsbr/schmidt
|
nxsbr
| 2023-11-19T19:28:34Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T19:08:26Z |
---
license: openrail
---
|
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miragepa/ANDROIDEN18
|
miragepa
| 2023-11-19T19:17:17Z | 0 | 0 |
[
"license:openrail",
"region:us"
] | null | 2023-11-19T19:16:32Z |
---
license: openrail
---
|
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] |
Jeryr/Yisus
|
Jeryr
| 2023-11-19T19:31:06Z | 0 | 0 |
[
"license:apache-2.0",
"region:us"
] | null | 2023-11-19T19:19:27Z |
---
license: apache-2.0
---
|
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Norod78/RickAndMorty-blip-captions-1024
|
Norod78
| 2023-11-19T19:36:23Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T19:36:05Z |
---
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 178602553.0
num_examples: 188
download_size: 178603589
dataset_size: 178602553.0
---
# Dataset Card for "RickAndMorty-blip-captions-1024"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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vibranium-dome/questions
|
vibranium-dome
| 2023-11-19T19:59:30Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-19T19:58:37Z |
---
license: mit
---
|
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] |
xuanzz/Drone
|
xuanzz
| 2023-11-19T21:08:14Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-19T20:23:57Z |
---
license: mit
dataset_info:
features:
- name: image
dtype: image
- name: text
dtype: string
splits:
- name: train
num_bytes: 381103939.086
num_examples: 1359
download_size: 379073342
dataset_size: 381103939.086
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
Credits: Taken from https://www.kaggle.com/datasets/dasmehdixtr/drone-dataset-uav
|
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Violetmae14/text-it-to-video-snap
|
Violetmae14
| 2023-11-19T20:24:43Z | 0 | 0 |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2023-11-19T20:24:43Z |
---
license: bigscience-bloom-rail-1.0
---
|
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] |
Yijia-Xiao/PII-PQA-raw
|
Yijia-Xiao
| 2023-11-19T20:36:49Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T20:28:04Z |
---
dataset_info:
features:
- name: Question
dtype: string
- name: Answer
dtype: string
- name: Protected Answer
dtype: string
splits:
- name: train
num_bytes: 7185732
num_examples: 42499
- name: test
num_bytes: 1274128
num_examples: 7504
download_size: 1212545
dataset_size: 8459860
---
# Dataset Card for "PPLM-PQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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] |
Yijia-Xiao/PPLM-PQA
|
Yijia-Xiao
| 2023-11-19T20:53:59Z | 0 | 0 |
[
"region:us"
] | null | 2023-11-19T20:53:54Z |
---
dataset_info:
features:
- name: instruction
dtype: string
- name: input
dtype: string
- name: output
dtype: string
- name: cleaned_output
dtype: string
splits:
- name: train
num_bytes: 8673197
num_examples: 42499
- name: test
num_bytes: 1536768
num_examples: 7504
download_size: 1233735
dataset_size: 10209965
---
# Dataset Card for "PPLM-PQA"
[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
|
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joshuasundance/govgis_nov2023-slim-spatial
|
joshuasundance
| 2023-11-23T00:18:04Z | 0 | 0 |
[
"size_categories:100K<n<1M",
"language:en",
"license:mit",
"gis",
"geospatial",
"doi:10.57967/hf/1369",
"region:us"
] | null | 2023-11-19T20:53:59Z |
---
license: mit
language:
- en
tags:
- gis
- geospatial
pretty_name: govgis_nov2023-slim-spatial
size_categories:
- 100K<n<1M
---
# govgis_nov2023-slim-spatial
🤖 This README was written by [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). 🤖
Introducing the govgis_nov2023-slim-spatial dataset, a carefully curated and georeferenced subset of the extensive [govgis_nov2023](https://huggingface.co/datasets/joshuasundance/govgis_nov2023) collection. This dataset stands out for its focus on geospatial data analysis, enriched with vector embeddings. While we have only explored a portion of this vast collection, the variety and richness of the content encountered have been remarkable, making it challenging to fully capture the dataset's breadth in a brief overview.
## Overview
The govgis_nov2023-slim-spatial dataset condenses key elements from the larger govgis_nov2023 collection into a more manageable format. It offers a glimpse into an extensive range of geospatial data types, all augmented with vector embeddings using [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5). Our exploration has revealed a staggering variety in the data, suggesting vast potential applications.
Key Features:
- **Diverse Geospatial Data Types:** The dataset includes samples of data like ecological data, census data, administrative boundaries, transportation networks, and land use maps, representing just a fraction of what's available.
- **Advanced Vector Search Capabilities:** Augmented with vector embeddings using [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) for sophisticated content discovery.
## Dataset Files
The dataset comprises two distinct files:
1. **`govgis_nov2023_slim_spatial.geoparquet`** This file offers core georeferenced spatial data, suitable for a broad range of analysis needs.
2. **`govgis_nov2023_slim_spatial_embs.geoparquet`:** A more comprehensive file with detailed vector embeddings, catering to more in-depth analytical demands.
This two-tiered approach allows users to tailor their engagement with the dataset based on their specific requirements.
## Benefits:
- **Selective Accessibility:** The dataset provides an accessible entry point to a seemingly endless variety of spatial data.
- **Efficient yet Comprehensive:** It distills a vast array of data into a more practical format without losing the essence of its diversity.
- **Untapped Application Potential:** The examples we provide are merely starting points; the dataset's true scope is far more extensive and varied.
- **Enhanced Analytical Depth:** Vector embeddings from [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) offer advanced data analysis capabilities.
## Use Cases:
Given the sheer variety of data we've glimpsed, the dataset is poised to serve a myriad of applications, far beyond the few examples we can confidently cite. It's designed to be adaptable to diverse analytical pursuits across different fields.
# Conclusion:
The govgis_nov2023-slim-spatial dataset is a thoughtfully distilled, georeferenced, and vector-embedded version of its more extensive counterpart. Our limited exploration has revealed an astonishing variety of data, hinting at a much broader scope of potential applications than we can definitively describe. This dual-file dataset is crafted to meet a wide spectrum of spatial data analysis needs, from the straightforward to the highly specialized, accommodating the extensive possibilities that lie within the realm of geospatial data.
|
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xuanzz/VideoCaptions
|
xuanzz
| 2023-11-19T21:13:02Z | 0 | 0 |
[
"license:mit",
"region:us"
] | null | 2023-11-19T21:08:44Z |
---
license: mit
---
|
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BangumiBase/sailormoon2010s
|
BangumiBase
| 2023-11-19T23:01:48Z | 0 | 0 |
[
"size_categories:1K<n<10K",
"license:mit",
"art",
"region:us"
] | null | 2023-11-19T21:14:39Z |
---
license: mit
tags:
- art
size_categories:
- 1K<n<10K
---
# Bangumi Image Base of Sailor Moon (2010s)
This is the image base of bangumi Sailor Moon (2010s), we detected 46 characters, 3463 images in total. The full dataset is [here](all.zip).
**Please note that these image bases are not guaranteed to be 100% cleaned, they may be noisy actual.** If you intend to manually train models using this dataset, we recommend performing necessary preprocessing on the downloaded dataset to eliminate potential noisy samples (approximately 1% probability).
Here is the characters' preview:
| # | Images | Download | Preview 1 | Preview 2 | Preview 3 | Preview 4 | Preview 5 | Preview 6 | Preview 7 | Preview 8 |
|:------|---------:|:---------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|:-------------------------------|
| 0 | 901 | [Download](0/dataset.zip) |  |  |  |  |  |  |  |  |
| 1 | 140 | [Download](1/dataset.zip) |  |  |  |  |  |  |  |  |
| 2 | 16 | [Download](2/dataset.zip) |  |  |  |  |  |  |  |  |
| 3 | 313 | [Download](3/dataset.zip) |  |  |  |  |  |  |  |  |
| 4 | 19 | [Download](4/dataset.zip) |  |  |  |  |  |  |  |  |
| 5 | 77 | [Download](5/dataset.zip) |  |  |  |  |  |  |  |  |
| 6 | 52 | [Download](6/dataset.zip) |  |  |  |  |  |  |  |  |
| 7 | 17 | [Download](7/dataset.zip) |  |  |  |  |  |  |  |  |
| 8 | 17 | [Download](8/dataset.zip) |  |  |  |  |  |  |  |  |
| 9 | 26 | [Download](9/dataset.zip) |  |  |  |  |  |  |  |  |
| 10 | 21 | [Download](10/dataset.zip) |  |  |  |  |  |  |  |  |
| 11 | 102 | [Download](11/dataset.zip) |  |  |  |  |  |  |  |  |
| 12 | 164 | [Download](12/dataset.zip) |  |  |  |  |  |  |  |  |
| 13 | 73 | [Download](13/dataset.zip) |  |  |  |  |  |  |  |  |
| 14 | 46 | [Download](14/dataset.zip) |  |  |  |  |  |  |  |  |
| 15 | 9 | [Download](15/dataset.zip) |  |  |  |  |  |  |  |  |
| 16 | 269 | [Download](16/dataset.zip) |  |  |  |  |  |  |  |  |
| 17 | 24 | [Download](17/dataset.zip) |  |  |  |  |  |  |  |  |
| 18 | 10 | [Download](18/dataset.zip) |  |  |  |  |  |  |  |  |
| 19 | 21 | [Download](19/dataset.zip) |  |  |  |  |  |  |  |  |
| 20 | 11 | [Download](20/dataset.zip) |  |  |  |  |  |  |  |  |
| 21 | 271 | [Download](21/dataset.zip) |  |  |  |  |  |  |  |  |
| 22 | 99 | [Download](22/dataset.zip) |  |  |  |  |  |  |  |  |
| 23 | 14 | [Download](23/dataset.zip) |  |  |  |  |  |  |  |  |
| 24 | 40 | [Download](24/dataset.zip) |  |  |  |  |  |  |  |  |
| 25 | 9 | [Download](25/dataset.zip) |  |  |  |  |  |  |  |  |
| 26 | 205 | [Download](26/dataset.zip) |  |  |  |  |  |  |  |  |
| 27 | 18 | [Download](27/dataset.zip) |  |  |  |  |  |  |  |  |
| 28 | 12 | [Download](28/dataset.zip) |  |  |  |  |  |  |  |  |
| 29 | 22 | [Download](29/dataset.zip) |  |  |  |  |  |  |  |  |
| 30 | 15 | [Download](30/dataset.zip) |  |  |  |  |  |  |  |  |
| 31 | 14 | [Download](31/dataset.zip) |  |  |  |  |  |  |  |  |
| 32 | 16 | [Download](32/dataset.zip) |  |  |  |  |  |  |  |  |
| 33 | 7 | [Download](33/dataset.zip) |  |  |  |  |  |  |  | N/A |
| 34 | 9 | [Download](34/dataset.zip) |  |  |  |  |  |  |  |  |
| 35 | 23 | [Download](35/dataset.zip) |  |  |  |  |  |  |  |  |
| 36 | 26 | [Download](36/dataset.zip) |  |  |  |  |  |  |  |  |
| 37 | 15 | [Download](37/dataset.zip) |  |  |  |  |  |  |  |  |
| 38 | 8 | [Download](38/dataset.zip) |  |  |  |  |  |  |  |  |
| 39 | 5 | [Download](39/dataset.zip) |  |  |  |  |  | N/A | N/A | N/A |
| 40 | 11 | [Download](40/dataset.zip) |  |  |  |  |  |  |  |  |
| 41 | 9 | [Download](41/dataset.zip) |  |  |  |  |  |  |  |  |
| 42 | 9 | [Download](42/dataset.zip) |  |  |  |  |  |  |  |  |
| 43 | 21 | [Download](43/dataset.zip) |  |  |  |  |  |  |  |  |
| 44 | 12 | [Download](44/dataset.zip) |  |  |  |  |  |  |  |  |
| noise | 245 | [Download](-1/dataset.zip) |  |  |  |  |  |  |  |  |
|
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