---
license: mit
tags:
- pokemon
---
# Pokemon Competitive Team Dataset

A curated image dataset featuring 6 specific Pokemon commonly used in competitive play: Arceus, Marshadow, Sandy Shocks, Slaking, Reshiram, and Magearna. This dataset is designed for training computer vision models to recognize these Pokemon in various contexts.

## Dataset Overview

| Pokemon      | Image Count | Percentage |
|--------------|-------------|------------|
| Arceus       | 644         | 49.7%      |
| Magearna     | 200         | 15.4%      |
| Slaking      | 152         | 11.7%      |
| Reshiram     | 118         | 9.1%       |
| Marshadow    | 101         | 7.8%       |
| Sandy Shocks | 75          | 5.8%       |
| **Total**    | **1,290**   | **100%**   |


## Pokemon Details

### Arceus
- **Type**: Normal (can change with Plates/Z-Crystals)
- **Generation**: IV (Diamond/Pearl)
- **Role**: Versatile support/offensive Pokemon
- **Images**: 644 (largest class - includes various formes)

### Marshadow
- **Type**: Fighting/Ghost
- **Generation**: VII (Sun/Moon)
- **Role**: Physical sweeper with unique typing
- **Images**: 101

### Sandy Shocks
- **Type**: Electric/Ground
- **Generation**: IX (Scarlet/Violet)
- **Role**: Paradox Pokemon, special attacker
- **Images**: 75 (smallest class - newer Pokemon)

### Slaking
- **Type**: Normal
- **Generation**: III (Ruby/Sapphire)
- **Role**: High-power attacker with Truant ability
- **Images**: 152

### Reshiram
- **Type**: Dragon/Fire
- **Generation**: V (Black/White)
- **Role**: Legendary special attacker
- **Images**: 118

### Magearna
- **Type**: Steel/Fairy
- **Generation**: VII (Sun/Moon)
- **Role**: Support/tank with Soul-Heart ability
- **Images**: 200

## Image Characteristics

### File Formats
- **Supported**: JPG, JPEG, PNG
- **Primary**: JPG (~70%), PNG (~30%)

### Image Sources
- Official Pokemon artwork
- Game screenshots (various Pokemon games)
- Trading card game artwork
- Anime screenshots
- Fan art (high-quality, recognizable)

### Image Quality
- **Resolution**: Varies (typically 200x200 to 1920x1080)
- **Aspect Ratios**: Mixed (square, 16:9, 4:3, portrait)
- **Content**: Pokemon-focused with various backgrounds
- **Lighting**: Natural variety from different sources

### Content Variety
- **Poses**: Multiple angles and positions
- **Contexts**: Battle scenes, portraits, environment shots
- **Styles**: Official art, game renders, anime style, realistic interpretations
- **Backgrounds**: Transparent, solid colors, natural environments, battle arenas

## Dataset Characteristics

### Class Imbalance
The dataset exhibits significant class imbalance:
- **Most represented**: Arceus (49.7% of total)
- **Least represented**: Sandy Shocks (5.8% of total)
- **Imbalance ratio**: 8.6:1 (Arceus vs Sandy Shocks)

### Balanced Training Strategy
When training models on this dataset, we recommend:
- **Balanced sampling**: Use equal samples per class per epoch
- **Weighted loss functions**: Account for class imbalance
- **Data augmentation**: Especially for underrepresented classes
- **Stratified splits**: Maintain class ratios in train/val/test splits

## Recommended Usage

### Training
```python
# Balanced sampling approach
samples_per_class = 75  # Based on smallest class (Sandy Shocks)
```

### Data Augmentation
Recommended augmentations for this dataset:
- Horizontal flips (Pokemon can face either direction)
- Rotation (±15 degrees)
- Color jitter (brightness, contrast, saturation)
- Random crops and resizing
- **Avoid**: Vertical flips (Pokemon don't appear upside down)

### Validation Strategy
- **Stratified sampling**: Maintain class proportions
- **Temporal split**: If timestamps available, use chronological splits
- **Balanced metrics**: Use balanced accuracy, not raw accuracy

## Technical Specifications

### File Organization
- Each Pokemon has its own subdirectory
- Consistent naming convention within directories
- No duplicate images across classes
- Clean filenames (no special characters)

### Data Quality
- **Manually curated**: All images verified for correct Pokemon
- **Deduplication**: Removed obvious duplicates
- **Quality filtering**: Excluded very low resolution or corrupted images
- **Labeling accuracy**: 100% (single Pokemon per image)

## Potential Challenges

### Class Imbalance
- Standard accuracy metrics may be misleading
- Model may be biased toward Arceus
- Requires careful sampling strategy

### Visual Similarity
- Some Pokemon share similar color schemes
- Legendary Pokemon may have similar poses
- Steel-type Pokemon (Magearna) may share metallic features

### Context Variation
- Wide variety of backgrounds and contexts
- Different art styles may confuse models
- Lighting and angle variations

## Evaluation Metrics

For this dataset, use:
- **Balanced Accuracy**: Accounts for class imbalance
- **Per-class Precision/Recall**: Individual Pokemon performance
- **Confusion Matrix**: Identify misclassification patterns
- **F1-scores**: Harmonic mean of precision/recall

## Use Cases

### Primary Applications
- **Pokemon recognition models**: Computer vision training
- **Competitive analysis**: Team composition recognition
- **Content filtering**: Pokemon-specific content moderation
- **Educational tools**: Pokemon identification applications

### Research Applications
- **Class imbalance handling**: Testing balancing techniques
- **Transfer learning**: Fine-tuning pre-trained models
- **Multi-class classification**: Benchmark dataset for 6-class problems

## Data Collection Methodology

1. **Pokemon Selection**: Chosen based on competitive viability and team synergy
2. **Source Diversification**: Multiple art styles and contexts
3. **Quality Control**: Manual verification of each image
4. **Deduplication**: Automated and manual duplicate removal
5. **Organization**: Systematic file naming and directory structure

## Licensing and Attribution

### Dataset License
This dataset is provided under the **Creative Commons Attribution 4.0 (CC BY 4.0)** license for research and educational purposes.

### Pokemon Copyright
- Pokemon characters are © The Pokémon Company/Nintendo
- This dataset is for non-commercial research/educational use
- Images sourced from publicly available content
- Fair use applies for research and educational purposes

### Attribution
If you use this dataset, please cite:

```bibtex
@dataset{pokemon_team_dataset,
  title={Pokemon Competitive Team Dataset},
  author={Steven Van Ingelgem},
  year={2025},
  url={https://huggingface.co/datasets/your-username/pokemon-team-dataset},
  note={6-class Pokemon image dataset for computer vision research}
}
```

## Download and Usage

### Requirements
- Python 3.8+
- PIL/Pillow for image loading
- OpenCV (optional, for advanced preprocessing)

### Loading Example
```python
from pathlib import Path
from PIL import Image

# Load dataset
dataset_path = Path("images")
pokemon_names = ["arceus", "marshadow", "sandy-shocks", "slaking", "reshiram", "magearna"]

# Example: Load all Arceus images
arceus_images = []
arceus_dir = dataset_path / "arceus"
for img_path in arceus_dir.glob("*"):
    if img_path.suffix.lower() in {'.jpg', '.jpeg', '.png'}:
        image = Image.open(img_path)
        arceus_images.append(image)

print(f"Loaded {len(arceus_images)} Arceus images")
```

## Version History

- **v1.0**: Initial release with 1,290 images across 6 Pokemon classes
- Focus on competitive Pokemon team composition
- Curated for computer vision model training

## Contact

For questions, issues, or contributions to this dataset, please contact:
- **Author**: Steven Van Ingelgem
- **Email**: steven@vaningelgem.be
- **GitHub**: https://github.com/svaningelgem

---

**Disclaimer**: This dataset is intended for research and educational purposes. All Pokemon-related content is the property of The Pokémon Company and Nintendo. Use in accordance with fair use guidelines and applicable copyright laws.