Skip to main content

A simple package packaging a pytorch dataloader for the CINIC10 dataset.

If you use it cite the original authors

@misc{https://doi.org/10.48550/arxiv.1810.03505,
  doi = {10.48550/ARXIV.1810.03505},
  url = {https://arxiv.org/abs/1810.03505},
  author = {Darlow, Luke N. and Crowley, Elliot J. and Antoniou, Antreas and Storkey, Amos J.},
  keywords = {Computer Vision and Pattern Recognition (cs.CV), Machine Learning (cs.LG), Machine Learning (stat.ML), FOS: Computer and information sciences, FOS: Computer and information sciences},
  title = {CINIC-10 is not ImageNet or CIFAR-10},
  publisher = {arXiv},
  year = {2018},
  copyright = {Creative Commons Attribution Share Alike 4.0 International}
}

and if you want to be nice, also this repo (although the code is borderline trivial so no hard feelings if not).

To use simply import

from pytorch_cinic.dataset import CINIC10

and then use like CIFAR10 (except that we use partition=train/valid/test instead of train=True/False)

Metadata

Release files for pytorch-cinic 0.0.6

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pytorch-cinic 0.0.6
File Size Uploaded Provenance Actions
pytorch_cinic-0.0.6.tar.gz 4.5 kB Without provenance Details for pytorch_cinic-0.0.6.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for pytorch-cinic 0.0.6
File Interpreter ABI Platform Actions
pytorch_cinic-0.0.6-py3-none-any.whl Python 3 none any Details for pytorch_cinic-0.0.6-py3-none-any.whl

Total release size: 9.8 kB

Release files / pytorch_cinic-0.0.6.tar.gz

Download URL pytorch_cinic-0.0.6.tar.gz
Size 4.5 kB
Tags Source
SHA-256 checksum
How to use checksums
6266c20eca0e10713cbc6e047ec649c2752859978b0f2cfb5845f052ed8ffdfe
BLAKE2b-256 checksum
How to use checksums
baa93d9f04398668480eae702a45cbd4fba956c08a95a56d60d4d4c99621f957
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.9.13

Release files / pytorch_cinic-0.0.6-py3-none-any.whl

Download URL pytorch_cinic-0.0.6-py3-none-any.whl
Size 5.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
3e50fc57b5970f6edc29a46516a1725553a92ad7ddd1c713b35f9806d8ebb294
BLAKE2b-256 checksum
How to use checksums
e97c2d416fd59d81e51450d48e4a2eda86df553ee7dc70634950df630d7f3843
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/4.0.1 CPython/3.9.13

Security for pytorch-cinic 0.0.6

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page