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Datasets:
czl
/
generated-imagenette

Tasks:
Image Classification
Image-to-Image
Modalities:
Image
Text
Formats:
json
Languages:
English
Size:
< 1K
Libraries:
Datasets
pandas
License:
Dataset card Viewer Files Community

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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.
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