metadata
license: other
license_name: apple-sample-code-license
license_link: LICENSE
A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-5B. Data Filtering Networks (DFNs) are small used to automatically filter large pools of uncurated data. This model was trained on 5B images that were filtered from a pool of 43B uncurated image-text pairs (12.8B image-text pairs from CommonPool-12.8B + 30B additional public image-text pairs).
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).
Model Details
- Model Type: Contrastive Image-Text, Zero-Shot Image Classification.
- Dataset: DFN-5b
- Papers:
- Data Filtering Networks: https://arxiv.org/abs/2309.17425
Model Metrics
| Eval Dataset | Metric |
|---|---|
| ImageNet 1k | 0.8344 |
| Caltech-101 | 0.954935 |
| CIFAR-10 | 0.9878 |
| CIFAR-100 | 0.9051 |
| CLEVR Counts | 0.2966 |
| CLEVR Distance | 0.2124 |
| Country211 | 0.343981 |
| Describable Textures | 0.706383 |
| EuroSAT | 0.654815 |
| FGVC Aircraft | 0.714055 |
| Food-101 | 0.956792 |
| GTSRB | 0.677514 |
| ImageNet Sketch | 0.727308 |
| ImageNet v2 | 0.773 |
| ImageNet-A | 0.6988 |
| ImageNet-O | 0.381 |
| ImageNet-R | 0.929367 |
| KITTI Vehicle Distance | 0.336146 |
| MNIST | 0.8579 |
| ObjectNet | 0.681275 |
| Oxford Flowers-102 | 0.899534 |
| Oxford-IIIT Pet | 0.965515 |
| Pascal VOC 2007 | 0.818309 |
| PatchCamelyon | 0.653625 |
| Rendered SST2 | 0.546403 |
| RESISC45 | 0.750476 |
| Stanford Cars | 0.957592 |
| STL-10 | 0.989 |
| SUN397 | 0.769149 |
| SVHN | 0.676168 |
| Flickr | 0.8645 |
| MSCOCO | 0.631112 |
| WinoGAViL | 0.556329 |
| iWildCam | 0.205549 |
| Camelyon17 | 0.705034 |
| FMoW | 0.207482 |
| Dollar Street | 0.699766 |
| GeoDE | 0.928184 |
| Average | 0.696139 |
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/DFN5B-CLIP-ViT-H-14')
tokenizer = get_tokenizer('hf-hub:apple/DFN5B-CLIP-ViT-H-14)
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}
}