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README.md

@@ -1,37 +1,151 @@
 ---
-dataset_info:
-  features:
-  - name: image
-    dtype: image
-  - name: image_id
-    dtype: int64
-  - name: noise_type
-    dtype: string
-  - name: count_squares
-    dtype: int64
-  - name: count_triangles
-    dtype: int64
-  - name: count_stars
-    dtype: int64
-  - name: total_shapes
-    dtype: int64
-  - name: bucket
-    dtype: int64
-  - name: bucket_name
-    dtype: string
-  - name: difficulty
-    dtype: string
-  - name: intended_counts
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 94003112.0
-    num_examples: 14360
-  download_size: 92976382
-  dataset_size: 94003112.0
-configs:
-- config_name: default
-  data_files:
-  - split: train
-    path: data/train-*
+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)