README.md

---
license: mit
task_categories:
  - image-classification
  - visual-question-answering
tags:
  - counting
  - shapes
  - vision
  - cognitive-science
  - psychology
size_categories:
  - 1K<n<10K
---

# Shape Counting Dataset

A dataset for evaluating shape counting abilities in vision models and humans.

## Dataset Description

This dataset contains images with varying numbers of **squares**, **triangles**, and **stars** on a white background. Each image is provided in multiple versions: the original clean image plus several noisy variants.

### Image Specifications
- **Size**: 256×256 pixels
- **Format**: Grayscale PNG
- **Shape size**: 18 pixels
- **Background**: White (255)
- **Shapes**: Black (0)

## Dataset Structure

### Fields

| Field | Type | Description |
|-------|------|-------------|
| `image` | Image | The shape image (original or noisy) |
| `image_id` | int | Unique ID for the base image (same across noise variants) |
| `noise_type` | string | Type of noise applied (see below) |
| `count_squares` | int | Number of squares in the image |
| `count_triangles` | int | Number of triangles in the image |
| `count_stars` | int | Number of stars in the image |
| `total_shapes` | int | Total number of shapes (sum of all counts) |
| `bucket` | int | Bucket number (1, 2, or 3) |
| `bucket_name` | string | Bucket description |
| `difficulty` | string | "medium" or "hard" |
| `intended_counts` | string | Original intended counts before placement |

### Buckets Explained

The dataset is organized into 3 buckets based on shape type complexity:

| Bucket | Name | Description |
|--------|------|-------------|
| **1** | `single_shape` | Only ONE type of shape (all squares, all triangles, or all stars) |
| **2** | `two_shapes` | TWO different shape types mixed together |
| **3** | `three_shapes` | ALL THREE shape types mixed together |

### Difficulty Levels

| Difficulty | Total Shapes | Description |
|------------|--------------|-------------|
| **medium** | 11-36 | Moderate counting difficulty |
| **hard** | 37-60 | Challenging counting task |

### Noise Types

Each image is provided in 8 variants:

| Noise Type | Description |
|------------|-------------|
| `original` | Clean image, no noise |
| `salt_pepper_medium` | 15% salt & pepper noise |
| `salt_pepper_heavy` | 25% salt & pepper noise |
| `salt_pepper_extreme` | 35% salt & pepper noise |
| `blur_heavy` | Gaussian blur (radius=3) |
| `blur_extreme` | Gaussian blur (radius=5) |
| `motion_blur` | Horizontal motion blur (size=9) |
| `motion_blur_heavy` | Horizontal motion blur (size=13) |

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("nooranis/shape-counting-dataset")

# Filter for only original (clean) images
original_only = dataset.filter(lambda x: x['noise_type'] == 'original')

# Filter for a specific noise type
blurry = dataset.filter(lambda x: x['noise_type'] == 'blur_heavy')

# Filter by difficulty
hard_images = dataset.filter(lambda x: x['difficulty'] == 'hard')

# Filter by bucket
single_shape = dataset.filter(lambda x: x['bucket'] == 1)
```

### Example: Get image and count

```python
from datasets import load_dataset

dataset = load_dataset("nooranis/shape-counting-dataset")

# Get a sample
sample = dataset['train'][0]

# Access the image
image = sample['image']  # PIL Image

# Access counts
print(f"Squares: {sample['count_squares']}")
print(f"Triangles: {sample['count_triangles']}")
print(f"Stars: {sample['count_stars']}")
print(f"Total: {sample['total_shapes']}")
```

### Example: Evaluate a vision model

```python
from datasets import load_dataset

dataset = load_dataset("nooranis/shape-counting-dataset")

# Get original images only
test_data = dataset['train'].filter(lambda x: x['noise_type'] == 'original')

for sample in test_data:
    image = sample['image']
    true_count = sample['count_stars']  # or count_squares, count_triangles
    
    # Your model prediction here
    predicted = your_model.count_stars(image)
    
    # Compare
    is_correct = (predicted == true_count)
```

## Dataset Statistics

- **Total images**: 1795 base images
- **With noise variants**: 14360 total rows
- **Noise types**: 8
- **Buckets**: 3
- **Difficulty levels**: 2 (medium, hard)