File size: 3,440 Bytes
7f520a1 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 | ---
dataset_info:
features:
- name: images
sequence: image
- name: problem
dtype: string
- name: answer
dtype: string
splits:
- name: train
num_examples: 39230
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
---
# StepCountQA-RL-Traj_11_50_Combined
## Dataset Description
StepCountQA-RL-Traj_11_50_Combined is a unified visual counting RL training dataset covering object counts from **11 to 50**. It merges two complementary 11-50 datasets into a single collection:
| Source | Samples | Description |
|--------|---------|-------------|
| StepCountQA-RL-Traj_11_50 | 9,508 | `<point>` JSON annotation trajectories, converted to numeric |
| StepCountQA-RL-Traj_11_50_NumericOnly | 29,722 | Pure numeric answer from SFT trajectories |
| **Combined** | **39,230** | Merged, deduplicated (0 overlap) |
All samples pair an original (non-annotated) image with a counting question and a **pure numeric answer string**.
## Dataset Structure
### Data Fields
| Field | Type | Description |
|-------|------|-------------|
| `images` | `Sequence(Image)` | One original image per sample |
| `problem` | `string` | Counting question with `<image>` tag |
| `answer` | `string` | Numeric count as string (e.g., `"27"`) |
### Format Example
```python
{
"images": [<PIL.Image>],
"problem": "<image>\nHow many people are in the image?",
"answer": "39"
}
```
## Dataset Statistics
- **Total samples**: 39,230
- **Count range**: 11 – 50
- **Unique images**: 39,230 (zero duplicates verified)
- **Total size**: ~8.95 GB (4 parquet shards)
### Count Distribution
| Count | Samples | Count | Samples |
|-------|---------|-------|---------|
| 11 | 2,537 | 31 | 660 |
| 12 | 3,297 | 32 | 676 |
| 13 | 1,863 | 33 | 591 |
| 14 | 2,147 | 34 | 558 |
| 15 | 2,145 | 35 | 621 |
| 16 | 2,011 | 36 | 687 |
| 17 | 1,372 | 37 | 469 |
| 18 | 1,732 | 38 | 484 |
| 19 | 1,235 | 39 | 464 |
| 20 | 1,621 | 40 | 670 |
| 21 | 1,136 | 41 | 425 |
| 22 | 1,072 | 42 | 474 |
| 23 | 967 | 43 | 359 |
| 24 | 1,182 | 44 | 383 |
| 25 | 976 | 45 | 424 |
| 26 | 907 | 46 | 364 |
| 27 | 753 | 47 | 323 |
| 28 | 879 | 48 | 432 |
| 29 | 627 | 49 | 341 |
| 30 | 940 | 50 | 426 |
## Comparison with Related Datasets
| Dataset | Samples | Count Range | Answer Format |
|---------|---------|-------------|---------------|
| StepCountQA-RL-Traj_11_50 | 9,508 | 11–50 | Numeric string |
| StepCountQA-RL-Traj_11_50_NumericOnly | 29,722 | 11–50 | Numeric string |
| **StepCountQA-RL-Traj_11_50_Combined** | **39,230** | **11–50** | **Numeric string** |
## Usage
```python
from datasets import load_dataset
ds = load_dataset("SI-Lab/StepCountQA-RL-Traj_11_50_Combined")
train = ds["train"]
print(train[0])
# {'images': [<PIL.Image>], 'problem': '<image>\nHow many people...', 'answer': '39'}
```
## Dataset Creation
Merged from two sources:
1. **StepCountQA-RL-Traj_11_50** (9,508 rows): Original `<point>` annotation dataset with numeric answers extracted
2. **StepCountQA-RL-Traj_11_50_NumericOnly** (29,722 rows): Converted from 11-50 SFT trajectory dataset
Merge process:
- Extracted image path field from both datasets (streaming, 50 rows/batch)
- Verified zero overlap (0 duplicate images between A and B)
- Streamed A then B in 100-row batches into 4 output shards
- Final row count verified: 9,508 + 29,722 = 39,230 ✓
## License
Please refer to the source dataset license for usage terms.
|