---
license: cc-by-nc-sa-4.0
task_categories:
  - image-classification
language:
  - en
tags:
  - dogs
  - stanford-dogs
  - augmented
  - computer-vision
  - bing-images
size_categories:
  - 10K<n<100K
---

# Stanford Dogs Amplified (Parquet Edition)

This dataset is an amplified and modernized version of the classic [Stanford Dogs Dataset](http://vision.stanford.edu/aditya86/ImageNetDogs/). It builds upon the original 120 dog breeds by automatically fetching, cleaning, and injecting thousands of new high-quality images scraped from Bing, filtered dynamically via YOLO object detection.

**This specific repository hosts the dataset natively in Hugging Face's optimized Parquet format.** 

This means:
1. It is a strictly "Image Classification" dataset (the original XML bounding boxes have been removed).
2. It contains exactly two columns: `image` (the binary picture) and `label` (the breed integer id).
3. It has been pre-split into an 80/20 train/test stratified split, ensuring perfect class balance.
4. It can be downloaded and streamed instantly.

### Quick Start

Load the dataset instantly into memory using the `datasets` library:

```python
from datasets import load_dataset

# This will download the Parquet files and load the Train & Test splits
dataset = load_dataset("fedehorl/stanford-dogs-amplified")

# View the structure
print(dataset)
# DatasetDict({
#     train: Dataset({
#         features: ['image', 'label'],
#         num_rows: 34157
#     })
#     test: Dataset({
#         features: ['image', 'label'],
#         num_rows: 8540
#     })
# })

# Access an image and its label
sample_image = dataset['train'][0]['image']
sample_label = dataset['train'].features['label'].int2str(dataset['train'][0]['label'])
print(f"This is a {sample_label}")
```

### Dataset Structure

*   **`image`**: A `PIL.Image.Image` object containing the RGB image.
*   **`label`**: A `datasets.ClassLabel` representing the dog breed (0 to 119).

### Data Splits

The dataset contains a total of ~42,697 images natively split into:
- **Train (80%)**: ~34,157 images
- **Test (20%)**: ~8,540 images

Both splits are strictly stratified by the `label` column to maintain an identical distribution of the 120 dog breeds across the training and testing sets.

### Dataset Creation & Augmentation Process

The augmented images were sourced by scraping Bing Images for modern pictures of the 120 breeds. To ensure dataset quality, a strict automated filtering pipeline was applied:

1.  **Format Constraints**: Only strictly RGB `.jpg` files were allowed. Files with alpha channels, or in `webp`/`avif` formats, were dropped or converted.
2.  **YOLO Validation**: Every scraped image was processed through the `yolo26m` model.
3.  **Confidence Threshold**: Only images where YOLO successfully detected a "Dog" were admitted into the final dataset.