Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.
This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:
Note: The v2 config correspond to the new 70/30 train/valid split (released in Dec 6 2019).
To use this dataset:
import tensorflow_datasets as tfds
ds = tfds.load('imagenette', split='train')
for ex in ds.take(4):
print(ex)
See the guide for more informations on tensorflow_datasets.
https://storage.googleapis.com/tfds-data/visualization/fig/imagenette-full-size-v2-1.0.0.png" alt="Visualization" width="500px">
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Imagenette is a subset of 10 easily classified classes from Imagenet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
This dataset was created by anil_adepu
It contains the following files:
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
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… See the full description on the dataset page: https://huggingface.co/datasets/czl/generated-imagenette.
The dataset used in the paper is CIFAR-10, Imagenette, and ImageNet.
This dataset was created by Jeremy Howard
Released under Data files © Original Authors
It contains the following files:
ytia0661/imagenette dataset hosted on Hugging Face and contributed by the HF Datasets community
This dataset was created by Adel Samigullin
This dataset was created by Jeremy Howard
Released under Data files © Original Authors
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Imagenette is a subset of 10 easily classified classes from the Imagenet dataset. It was originally prepared by Jeremy Howard of FastAI. The objective behind putting together a small version of the Imagenet dataset was mainly because running new ideas/algorithms/experiments on the whole Imagenet take a lot of time.
This version of the dataset allows researchers/practitioners to quickly try out ideas and share with others. The dataset comes in three variants:
Note: The v2 config correspond to the new 70/30 train/valid split (released in Dec 6 2019).
To use this dataset:
import tensorflow_datasets as tfds
ds = tfds.load('imagenette', split='train')
for ex in ds.take(4):
print(ex)
See the guide for more informations on tensorflow_datasets.
https://storage.googleapis.com/tfds-data/visualization/fig/imagenette-full-size-v2-1.0.0.png" alt="Visualization" width="500px">