| --- |
| 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 networks 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 |
| - **Samples Seen:** 39B (224 x 224) + 5B (384 x 384) |
| ## Model Metrics |
| | dataset | metric | |
| |:-----------------------|---------:| |
| | ImageNet 1k | 0.84218 | |
| | Caltech-101 | 0.954479 | |
| | CIFAR-10 | 0.9879 | |
| | CIFAR-100 | 0.9041 | |
| | CLEVR Counts | 0.362467 | |
| | CLEVR Distance | 0.206067 | |
| | Country211 | 0.37673 | |
| | Describable Textures | 0.71383 | |
| | EuroSAT | 0.608333 | |
| | FGVC Aircraft | 0.719938 | |
| | Food-101 | 0.963129 | |
| | GTSRB | 0.679018 | |
| | ImageNet Sketch | 0.73338 | |
| | ImageNet v2 | 0.7837 | |
| | ImageNet-A | 0.7992 | |
| | ImageNet-O | 0.3785 | |
| | ImageNet-R | 0.937633 | |
| | KITTI Vehicle Distance | 0.38256 | |
| | MNIST | 0.8372 | |
| | ObjectNet <sup>1</sup> | 0.796867 | |
| | Oxford Flowers-102 | 0.896834 | |
| | Oxford-IIIT Pet | 0.966841 | |
| | Pascal VOC 2007 | 0.826255 | |
| | PatchCamelyon | 0.695953 | |
| | Rendered SST2 | 0.566722 | |
| | RESISC45 | 0.755079 | |
| | Stanford Cars | 0.959955 | |
| | STL-10 | 0.991125 | |
| | SUN397 | 0.772799 | |
| | SVHN | 0.671251 | |
| | Flickr | 0.8808 | |
| | MSCOCO | 0.636889 | |
| | WinoGAViL | 0.571813 | |
| | iWildCam | 0.224911 | |
| | Camelyon17 | 0.711536 | |
| | FMoW | 0.209024 | |
| | Dollar Street | 0.71729 | |
| | GeoDE | 0.935699 | |
| | **Average** | **0.709421** | |
|
|
|
|
| [1]: Center-crop pre-processing used for ObjectNet (squashing results in lower accuracy of 0.737) |
| ## 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-14-384') |
| tokenizer = get_tokenizer('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 |
| ```bibtex |
| @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} |
| } |
| |
| ``` |
