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README.md
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---
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license: mit
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dataset_info:
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features:
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- name: image
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dtype: image
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- name: commands
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list:
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- name: shape
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dtype: string
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- name: class
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dtype: string
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- name: position_x
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dtype: float64
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- name: position_y
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dtype: float64
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- name: position_z
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dtype: int64
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- name: width
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dtype: float64
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- name: height
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dtype: float64
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- name: color
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sequence: int64
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splits:
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- name: train
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num_bytes: 1838065
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num_examples: 1000
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download_size: 789830
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dataset_size: 1838065
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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---
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license: mit
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dataset_info:
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features:
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- name: image
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dtype: image
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- name: commands
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list:
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- name: shape
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dtype: string
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- name: class
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dtype: string
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- name: position_x
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dtype: float64
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- name: position_y
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dtype: float64
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- name: position_z
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dtype: int64
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- name: width
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dtype: float64
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- name: height
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dtype: float64
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- name: color
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sequence: int64
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splits:
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- name: train
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num_bytes: 1838065
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num_examples: 1000
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download_size: 789830
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dataset_size: 1838065
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configs:
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- config_name: default
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data_files:
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- split: train
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path: data/train-*
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language:
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- en
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tags:
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- synthetic
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size_categories:
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- 1K<n<10K
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task_categories:
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- text-to-image
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- text-generation
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pretty_name: Houses
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---
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# Houses
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This synthetic dataset contains synthetic images of simple houses and their corresponding drawing instructions. It is designed for training models to predict missing elements in visual sequences to facilitate systems such as Copilots.
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## Features
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- **Images**: 256x256 images representing house drawings.
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- **Commands**: structured commands in JSON format describing the components of the house (e.g., body, roof, windows).
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## Usage
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This dataset can be used to train machine learning models for tasks such as:
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- sequence completion;
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- image generation from structured commands.
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You can also use code from this [repository](https://github.com/vvmatorin/houses) to regenerate dataset of the same format according to your requirements.
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