OpenCLIP
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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).

These weights are directly usable in OpenCLIP (image + text).

Model Details

  • Model Type: Contrastive Image-Text, Zero-Shot Image Classification.
  • Dataset: DFN-2b
  • Papers:
  • Examples Seen: 12.8B

Model Metrics

Dataset Metric
ImageNet 1k 0.76236
Caltech-101 0.942894
CIFAR-10 0.9672
CIFAR-100 0.8347
CLEVR Counts 0.232333
CLEVR Distance 0.245267
Country211 0.19545
Describable Textures 0.575532
EuroSAT 0.54
FGVC Aircraft 0.248503
Food-101 0.91303
GTSRB 0.469913
ImageNet Sketch 0.620684
ImageNet v2 0.682
ImageNet-A 0.482133
ImageNet-O 0.493
ImageNet-R 0.830967
KITTI Vehicle Distance 0.192686
MNIST 0.782
ObjectNet 0.631851
Oxford Flowers-102 0.819895
Oxford-IIIT Pet 0.936907
Pascal VOC 2007 0.788528
PatchCamelyon 0.521545
Rendered SST2 0.486546
RESISC45 0.61381
Stanford Cars 0.90735
STL-10 0.97525
SUN397 0.714162
SVHN 0.598955
Flickr 0.7728
MSCOCO 0.518773
WinoGAViL 0.541748
iWildCam 0.155574
Camelyon17 0.499283
FMoW 0.141149
Dollar Street 0.625
GeoDE 0.891023
Average 0.609232

Model Usage

With OpenCLIP

import torch
import torch.nn.functional as F
from urllib.request import urlopen
from PIL import Image
from open_clip import create_model_from_pretrained, get_tokenizer 

model, preprocess = create_model_from_pretrained('hf-hub:apple/DFN2B-CLIP-ViT-B-16')
tokenizer = get_tokenizer('ViT-B-16')

image = Image.open(urlopen(
    'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
image = preprocess(image).unsqueeze(0)

labels_list = ["a dog", "a cat", "a donut", "a beignet"]
text = tokenizer(labels_list, context_length=model.context_length)

with torch.no_grad(), torch.cuda.amp.autocast():
    image_features = model.encode_image(image)
    text_features = model.encode_text(text)
    image_features = F.normalize(image_features, dim=-1)
    text_features = F.normalize(text_features, dim=-1)

    text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias)

zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]]))
print("Label probabilities: ", zipped_list)

Citation

@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}
}
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