README.md

---
dataset_info:
  features:
  - name: images
    sequence: image
  - name: problem
    dtype: string
  - name: answer
    dtype: string
  splits:
  - name: train
    num_bytes: 32158573685
    num_examples: 192980
  download_size: 0
  dataset_size: 32158573685
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
---

# StepCountQA-RL-Dense-Plus Dataset

## Dataset Description

StepCountQA-RL-Dense-Plus is a carefully filtered subset of StepCountQA-RL, containing **complete reasoning chains** where the final count is between 11 and 50.

**Key Feature**: Each sequence includes **ALL reasoning steps** from count=1 to the final count (11-50), making it ideal for training models on dense counting scenarios with complete reasoning processes.

## Dataset Statistics

- **Training Samples**: 192,980
- **Sequences**: ~7,800 complete reasoning chains
- **Count Range**: 11-50 (final count)
- **Average Steps per Sequence**: ~24 steps

## Data Structure

### Complete Reasoning Chain Format

Each counting task contains a full reasoning chain from the first to the last point:

```
image.jpg          -> count=1, {"point_2d": [x1, y1], "label": "object", "count_number": 1}
image_1.jpg        -> count=2, {"point_2d": [x2, y2], "label": "object", "count_number": 2}
image_2.jpg        -> count=3, {"point_2d": [x3, y3], "label": "object", "count_number": 3}
...
image_N.jpg        -> count=N+1 (where N+1 is between 11-50)
```

### Data Fields

- `images`: A sequence of images (typically one image per sample)
- `problem`: Question text with reasoning instructions (`<image>\nHow many [objects] are in the image?\n...`)
- `answer`: 
  - During reasoning steps: JSON format `{"point_2d": [x, y], "label": "...", "count_number": N}`
  - Final answer: Simple number string `"N"`

## Dataset Characteristics

### 1. Complete Reasoning Chains
- Every sequence starts from count=1
- Includes all intermediate steps
- Ends with final count between 11-50

### 2. Dense Counting Scenarios
- Focus on moderately dense object counts (11-50 objects)
- Suitable for training on challenging counting tasks
- Balances complexity and tractability

### 3. Diverse Object Types
- People, vehicles, everyday objects
- Fine-grained object parts (hands, heads, etc.)
- Various scenes and contexts

## Usage Example

```python
from datasets import load_dataset

# Load the dataset
dataset = load_dataset("MING-ZCH/StepCountQA-RL-Dense-Plus")

# Access training data
train_data = dataset["train"]

# View a sample
sample = train_data[0]
print(sample['problem'])
print(sample['answer'])
# The answer may be JSON (intermediate step) or a number (final answer)
```

## Training Recommendations

This dataset is particularly useful for:
- **Incremental counting models**: Learn to count step-by-step
- **Dense object detection**: Train on moderately crowded scenes
- **Reasoning consistency**: Ensure models maintain coherent reasoning chains
- **Point-based annotation**: Learn precise spatial localization

## Citation

If you use this dataset, please cite the original StepCountQA-RL dataset.

## License

Follows the same license as the original StepCountQA-RL dataset.