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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 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 Copilot. |
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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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- commands generation from visual inputs. |
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You can also use code from this [repository](https://github.com/vvmatorin/houses) to generate a dataset of the same format according to your own requirements. |
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