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dataset_info:
features:
- name: image
dtype: image
- name: task
dtype: string
- name: label
dtype: string
- name: description
dtype: string
- name: dataset_type
dtype: string
- name: question
dtype: string
- name: options
list: string
- name: shape
dtype: string
- name: color
dtype: string
- name: size_class
dtype: string
- name: location
dtype: string
- name: angle
dtype: string
splits:
- name: angle
num_bytes: 375221
num_examples: 240
- name: angle_count
num_bytes: 156342
num_examples: 100
- name: color
num_bytes: 375221
num_examples: 240
- name: color_count
num_bytes: 156342
num_examples: 100
- name: count
num_bytes: 156342
num_examples: 100
- name: location
num_bytes: 375221
num_examples: 240
- name: location_count
num_bytes: 156342
num_examples: 100
- name: occlusion
num_bytes: 156342
num_examples: 100
- name: position
num_bytes: 156342
num_examples: 100
- name: shape
num_bytes: 375221
num_examples: 240
- name: shape_count
num_bytes: 156342
num_examples: 100
- name: size
num_bytes: 375221
num_examples: 240
- name: size_count
num_bytes: 156342
num_examples: 100
- name: train
num_bytes: 3128593
num_examples: 2000
download_size: 3659488
dataset_size: 6255434
configs:
- config_name: default
data_files:
- split: angle
path: data/angle-*
- split: angle_count
path: data/angle_count-*
- split: color
path: data/color-*
- split: color_count
path: data/color_count-*
- split: count
path: data/count-*
- split: location
path: data/location-*
- split: location_count
path: data/location_count-*
- split: occlusion
path: data/occlusion-*
- split: position
path: data/position-*
- split: shape
path: data/shape-*
- split: shape_count
path: data/shape_count-*
- split: size
path: data/size-*
- split: size_count
path: data/size_count-*
- split: train
path: data/train-*
---
## Dataset Description
This dataset contains two types of tasks:
### Single-Object Tasks (5 tasks, 1,200 samples)
- **Shape**: Identify object shape (star, diamond, triangle, square)
- **Color**: Identify object color (red, green, blue, yellow)
- **Size**: Identify object size (xs, s, l, xl)
- **Location**: Identify object location (upper_left, upper_right, lower_left, lower_right)
- **Angle**: Identify triangle orientation (0°, 45°, 90°, 135°)
### Multi-Object Tasks (3 tasks, 300 samples)
- **Count**: Count objects by color
- **Position**: Determine relative position (left, right, above, below)
- **Occlusion**: Identify which object is on top
## Key Features
- **White background** for clarity
- **Randomized attributes**: Each task randomizes non-target attributes to prevent shortcuts
- **Description field**: Enables text-only experiments (image vs. text-only comparison)
- **Balanced classes**: Equal samples per class within each task
## Dataset Structure
```python
{
"image": PIL.Image,
"task": str, # Task name
"label": str, # Ground truth label
"description": str, # Text description of the image
"dataset_type": str, # "single" or "multi"
"question": str, # Question text (multi-object only)
"options": List[str], # Answer options (multi-object only)
"shape": str, # Object shape (if applicable)
"color": str, # Object color (if applicable)
"size_class": str, # Size class (if applicable)
"location": str, # Location (if applicable)
"angle": str, # Angle (if applicable)
}
```
## Usage
```python
from datasets import load_dataset
# Load entire dataset
ds = load_dataset("Hanoi0126/visual-object-attributes")
# Load specific task
ds_color = load_dataset("Hanoi0126/visual-object-attributes", split="color")
```
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