--- license: apple-amlr 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 1 | 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} } ```
