BIG-Bench but it doesn't require the hellish dependencies (tensorflow, pypi-bigbench, protobuf) of the official version. dataset = load_dataset("tasksource/bigbench",'movie_recommendation')

Code to reproduce: https://colab.research.google.com/drive/1MKdLdF7oqrSQCeavAcsEnPdI85kD0LzU?usp=sharing Datasets are capped to 50k examples to keep things light. I also removed the default split when train was available also to save space, as default=train+val. @article{srivastava2022beyond… See the full description on the dataset page: https://huggingface.co/datasets/NJUDeepEngine/bigbench_jsonl.

The PlantVillage dataset consists of 54303 healthy and unhealthy leaf images divided into 38 categories by species and disease.

NOTE: The original dataset is not available from the original source (plantvillage.org), therefore we get the unaugmented dataset from a paper that used that dataset and republished it. Moreover, we dropped images with Background_without_leaves label, because these were not present in the original dataset.

Original paper URL: https://arxiv.org/abs/1511.08060 Dataset URL: https://data.mendeley.com/datasets/tywbtsjrjv/1

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('plant_village', 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/plant_village-1.0.2.png" alt="Visualization" width="500px">

Context

MNIST is a subset of a larger set available from NIST (it's copied from http://yann.lecun.com/exdb/mnist/)

Content

The MNIST database of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples. . Four files are available:

• train-images-idx3-ubyte.gz: training set images (9912422 bytes)
• train-labels-idx1-ubyte.gz: training set labels (28881 bytes)
• t10k-images-idx3-ubyte.gz: test set images (1648877 bytes)
• t10k-labels-idx1-ubyte.gz: test set labels (4542 bytes)

How to read

See sample MNIST reader

Acknowledgements

• Yann LeCun, Courant Institute, NYU
• Corinna Cortes, Google Labs, New York
• Christopher J.C. Burges, Microsoft Research, Redmond

Inspiration

Many methods have been tested with this training set and test set (see http://yann.lecun.com/exdb/mnist/ for more details)