dataset2metadata

Process a downloaded dataset (currently a webdataset generated by img2dataset). This repo implements the metadata processing for the DataComp project.

Please see the examples/ folder for ways in which dataset2metadata used, including slurm examples!

Installation

pip install git+https://github.com/mlfoundations/dataset2metadata

Note: pypi support coming soon!

basic.yml

Here we provide a basic yml, which should run on most consumer hardware. This includes computing:

OpenAI CLIP ViT-B/32 image, text features and CLIP scores

models: # model directives, specifying the models to instantiate
  - oai-clip-vit-b32
postprocess_columns: # postprocessing directives
  - oai-clip-vit-b32-score
postprocess_features: # saved in an npz format
  - oai-clip-vit-b32-image
  - oai-clip-vit-b32-text
additional_fields: # fields in a webdataset json to carry over into the metadata
  - uid
  - url
  - caption
  - original_width
  - original_height
  - sha256
nworkers: 2
batch_size: 512
device: 0
input_tars: "path/to/my/tars/000057{17..19}.tar" # braceexpand suported, can also be s3 paths
output_metadata_dir: "path/to/my/ouput/metadata" # can be arbitrary path
custom_pypath: null # if model, preprocessors, postprocessors not known, look in this python file for user provided custom implementation
reprocess: True # if true will process from scratch, else will just process tars not already processed

Run:

dataset2metadata --yml basic.yml

datacomp.yml

Here we provide a default yml, which is implements DataComp preprocessing. This includes computing:

OpenAI CLIP ViT-B/32 image, text features and CLIP scores
OpenAI CLIP ViT-L/14 image, text features and CLIP scores
detoxify text toxicity scores
NSFW image filtering (custom trained classifier on CLIP ViT-L/14 images featues)
ISC Descriptor features and near-duplicate scores against the DataComp evaluation sets.

models: # model directives, specifying the models to instantiate
  - oai-clip-vit-b32
  - oai-clip-vit-l14
  - nsfw-detoxify
  - nsfw-image-oai-clip-vit-l-14
  - faces-scrfd10g
  - dedup-isc-ft-v107
postprocess_columns: # postprocessing directives
  - oai-clip-vit-b32-score
  - oai-clip-vit-l14-score
  - nsfw-detoxify-score
  - nsfw-image-score
  - face-boxes
  - dedup-isc-ft-v107-score
postprocess_features: # saved in an npz format
  - oai-clip-vit-b32-image
  - oai-clip-vit-b32-text
  - oai-clip-vit-l14-image
  - oai-clip-vit-l14-text
  - dedup-isc-ft-v107-image
additional_fields: # fields in a webdataset json to carry over into the metadata
  - uid
  - url
  - caption
  - original_width
  - original_height
  - sha256
nworkers: 2
batch_size: 512
device: 0
input_tars: "path/to/my/tars/000057{17..19}.tar" # braceexpand suported, can also be s3 paths
output_metadata_dir: "path/to/my/ouput/metadata" # can be arbitrary path
custom_pypath: null # if model, preprocessors, postprocessors not known, look in this python file for user provided custom implementation
reprocess: True # if true will process from scratch, else will just process tars not already processed

Run:

dataset2metadata --yml datacomp.yml

Note, this workload needs ~40GB of GPU VRAM.

Citation

If you found this repository useful, please consider citing:

@article{datacomp,
  title={DataComp: In search of the next generation of multimodal datasets},
  author={Samir Yitzhak Gadre, Gabriel Ilharco, Alex Fang, Jonathan Hayase, Georgios Smyrnis, Thao Nguyen, Ryan Marten, Mitchell Wortsman, Dhruba Ghosh, Jieyu Zhang, Eyal Orgad, Rahim Entezari, Giannis Daras, Sarah Pratt, Vivek Ramanujan, Yonatan Bitton, Kalyani Marathe, Stephen Mussmann, Richard Vencu, Mehdi Cherti, Ranjay Krishna, Pang Wei Koh, Olga Saukh, Alexander Ratner, Shuran Song, Hannaneh Hajishirzi, Ali Farhadi, Romain Beaumont, Sewoong Oh, Alex Dimakis, Jenia Jitsev, Yair Carmon, Vaishaal Shankar, Ludwig Schmidt},
  journal={arXiv preprint arXiv:2304.14108},
  year={2023}
}

Acknowlegements