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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.
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