---
license: apache-2.0
task_categories:
- image-classification
- image-to-image
language:
- en
pretty_name: Generated Imagenette
size_categories:
- 1K<n<10K
---
# Generated Imagenette Dataset

## Description

This repository contains the dataset used for the `generative-data-augmentation` project. The dataset is organized as follows:

## Dataset Structure

- `analysis/`: This directory contains analysis related to the dataset.
- `metadata/`: This directory contains the list of file path used for the `Synthetic (Noisy)` and `Synthetic (Clean)` datasets.
- `synthetic/`: This directory contains the image files. Each folder represents a class.

## Metadata Files

- `metadata/synthetic-cleaned.txt`: This file contains the file paths for the `Synthetic (Clean)` dataset.
- `metadata/synthetic-noisy.txt`: This file contains the file paths for the `Synthetic (Noisy)` dataset.

## Analysis Files

- `analysis/imageGen_trace_clip.csv`: This file contains the trace data for the generated images, with similarity scores.
- `analysis/imageGen_trace_input_clip_mean.csv`: This file contains the pivot table for the mean CLIP similarity scores for the generated images by interpolation steps.
- `analysis/imageGen_trace_ssim_i_avg.csv`: This file contains the pivot table for the mean SSIM scores for the generated images by interpolation steps.
- `analysis/imageGen_trace.csv`: This file contains the trace data for the generated images, without similarity scores.
- `analysis/ori_synth_regplot.svg`: This file contains the regression plot for the similarity scores between the original and synthetic images.
- `analysis/ssim_regplot.svg`: This file contains the regression plot for the SSIM scores between the original and synthetic images.
- `analysis/text_synth_regplot.svg`: This file contains the regression plot for the similarity scores between the synthetic images and the text embeddings.
- `analysis/val.json`: This file contains the evaluation results of the baseline classifier on the validation dataset, which identified the top 5 misclassified classes for each class in the dataset.