Datasets:
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---
license: mit
task_categories:
- image-classification
tags:
- synthetic-data
- text-to-image
- data-augmentation
- zero-shot
- few-shot
- computer-vision
pretty_name: SynthBench
size_categories:
- 1K<n<10K
---
# SynthBench
Benchmark dataset for evaluating synthetic data generation methods for visual classification. Contains real iPhone photos, FLUX text-to-image generated images, and programmatically augmented synthetic images across 6 object classes.
## Classes
mouse, pen, phone, laptop, water bottle, Rubik's cube
## Dataset Structure
```
data/
├── raw/ # 308 raw iPhone photos (HEIC converted to JPEG)
├── real/ # 309 preprocessed images (224x224), train/test split
│ ├── train/
│ └── test/
├── synthetic_t2i/ # 1,774 FLUX text-to-image generated images
├── synthetic_aug/ # 1,801 programmatically augmented synthetic images
└── synthetic_t2i_lowdiv/ # 597 low-diversity T2I images (ablation)
models/
├── real_baseline.pth # ResNet-18 trained on real data only
├── t2i_zero_shot.pth # ResNet-18 trained on T2I synthetic data (zero-shot)
└── aug_zero_shot.pth # ResNet-18 trained on augmented synthetic data (zero-shot)
```
## Usage
```python
from huggingface_hub import snapshot_download
# Download everything
snapshot_download(repo_id="LakshC/SynthBench", repo_type="dataset")
# Download only real images
snapshot_download(repo_id="LakshC/SynthBench", repo_type="dataset", allow_patterns="data/real/*")
```
Or via CLI:
```bash
huggingface-cli download LakshC/SynthBench --repo-type dataset
```
## Associated Code
GitHub: [https://github.com/LakshC/SynthBench](https://github.com/LakshC/SynthBench)
## Model
All checkpoints are ResNet-18 (via `timm`), fine-tuned for 6-class classification.
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