DFN Models + Data
Collection
CLIP Models trained using DFN-2B/DFN-5B datasets
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7 items
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Updated
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12
A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-2B. Data Filtering Networks (DFNs) are small networks used to automatically filter large pools of uncurated data. This model was trained on 2B images that were filtered from a pool of 12.8B uncurated image-text pairs (12.8B image-text pairs from CommonPool-12.8B).
This model has been converted to PyTorch from the original JAX checkpoints from Axlearn (https://github.com/apple/axlearn). These weights are directly usable in OpenCLIP (image + text).
Eval Dataset | Metric |
---|---|
ImageNet 1k | 0.8219 |
Caltech-101 | 0.9500 |
CIFAR-10 | 0.9864 |
CIFAR-100 | 0.8934 |
CLEVR Counts | 0.3403 |
CLEVR Distance | 0.2321 |
Country211 | 0.3198 |
Describable Textures | 0.6681 |
EuroSAT | 0.6819 |
FGVC Aircraft | 0.4829 |
Food-101 | 0.9498 |
GTSRB | 0.6329 |
ImageNet Sketch | 0.7043 |
ImageNet v2 | 0.7570 |
ImageNet-A | 0.6745 |
ImageNet-O | 0.3605 |
ImageNet-R | 0.9184 |
KITTI Vehicle Distance | 0.2391 |
MNIST | 0.8745 |
ObjectNet | 0.7477 |
Oxford Flowers-102 | 0.8784 |
Oxford-IIIT Pet | 0.9611 |
Pascal VOC 2007 | 0.8472 |
PatchCamelyon | 0.6418 |
Rendered SST2 | 0.5815 |
RESISC45 | 0.7300 |
Stanford Cars | 0.9465 |
STL-10 | 0.9889 |
SUN397 | 0.7594 |
SVHN | 0.6573 |
Flickr | 0.8467 |
MSCOCO | 0.5957 |
WinoGAViL | 0.5551 |
iWildCam | 0.1857 |
Camelyon17 | 0.6540 |
FMoW | 0.1824 |
Dollar Street | 0.6822 |
GeoDE | 0.9253 |
Average | 0.68039 |
@article{fang2023data,
title={Data Filtering Networks},
author={Fang, Alex and Jose, Albin Madappally and Jain, Amit and Schmidt, Ludwig and Toshev, Alexander and Shankar, Vaishaal},
journal={arXiv preprint arXiv:2309.17425},
year={2023}
}