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--- |
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license: other |
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license_name: apple-sample-code-license |
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license_link: LICENSE |
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--- |
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A CLIP (Contrastive Language-Image Pre-training) model trained on DFN-2B. |
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Data Filtering Networks (DFNs) are small networks used to automatically filter large pools of uncurated data. |
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This model was trained on 2B images that were filtered from a pool of 12.8B uncurated image-text pairs |
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(12.8B image-text pairs from CommonPool-12.8B). |
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This model has been converted to PyTorch from the original JAX checkpoints from Axlearn (https://github.com/apple/axlearn). |
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These weights are directly usable in OpenCLIP (image + text). |
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## Model Details |
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- **Model Type:** Contrastive Image-Text, Zero-Shot Image Classification. |
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- **Dataset:** DFN-2b |
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- **Papers:** |
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- Data Filtering Networks: https://arxiv.org/abs/2309.17425 |
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- **Examples Seen:** 12.8B |
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## Model Metrics |
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| Eval Dataset | Metric | |
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|:-----------------------|---------:| |
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| ImageNet 1k | 0.81396 | |
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| Caltech-101 | 0.953141 | |
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| CIFAR-10 | 0.9836 | |
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| CIFAR-100 | 0.8835 | |
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| CLEVR Counts | 0.3338 | |
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| CLEVR Distance | 0.248733 | |
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| Country211 | 0.28237 | |
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| Describable Textures | 0.66117 | |
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| EuroSAT | 0.646296 | |
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| FGVC Aircraft | 0.395945 | |
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| Food-101 | 0.945861 | |
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| GTSRB | 0.616152 | |
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| ImageNet Sketch | 0.683311 | |
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| ImageNet v2 | 0.7453 | |
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| ImageNet-A | 0.6676 | |
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| ImageNet-O | 0.3915 | |
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| ImageNet-R | 0.900033 | |
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| KITTI Vehicle Distance | 0.201125 | |
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| MNIST | 0.8468 | |
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| ObjectNet | 0.739367 | |
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| Oxford Flowers-102 | 0.865822 | |
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| Oxford-IIIT Pet | 0.954941 | |
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| Pascal VOC 2007 | 0.81644 | |
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| PatchCamelyon | 0.63028 | |
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| Rendered SST2 | 0.551345 | |
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| RESISC45 | 0.733175 | |
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| Stanford Cars | 0.947146 | |
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| STL-10 | 0.976625 | |
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| SUN397 | 0.754565 | |
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| SVHN | 0.653503 | |
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| Flickr | 0.8244 | |
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| MSCOCO | 0.570363 | |
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| WinoGAViL | 0.551645 | |
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| iWildCam | 0.18877 | |
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| Camelyon17 | 0.626179 | |
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| FMoW | 0.222137 | |
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| Dollar Street | 0.688084 | |
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| GeoDE | 0.91023 | |
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| **Average** | **0.668558** | |
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## Model Usage |
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### With OpenCLIP |
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``` |
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import torch |
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import torch.nn.functional as F |
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from urllib.request import urlopen |
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from PIL import Image |
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from open_clip import create_model_from_pretrained, get_tokenizer |
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model, preprocess = create_model_from_pretrained('hf-hub:apple/DFN2B-CLIP-ViT-L-14') |
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tokenizer = get_tokenizer('ViT-L-14') |
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image = Image.open(urlopen( |
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'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png' |
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)) |
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image = preprocess(image).unsqueeze(0) |
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labels_list = ["a dog", "a cat", "a donut", "a beignet"] |
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text = tokenizer(labels_list, context_length=model.context_length) |
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with torch.no_grad(), torch.cuda.amp.autocast(): |
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image_features = model.encode_image(image) |
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text_features = model.encode_text(text) |
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image_features = F.normalize(image_features, dim=-1) |
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text_features = F.normalize(text_features, dim=-1) |
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text_probs = torch.sigmoid(image_features @ text_features.T * model.logit_scale.exp() + model.logit_bias) |
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zipped_list = list(zip(labels_list, [round(p.item(), 3) for p in text_probs[0]])) |
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print("Label probabilities: ", zipped_list) |
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``` |
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## Citation |
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```bibtex |
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@article{fang2023data, |
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title={Data Filtering Networks}, |
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author={Fang, Alex and Jose, Albin Madappally and Jain, Amit and Schmidt, Ludwig and Toshev, Alexander and Shankar, Vaishaal}, |
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journal={arXiv preprint arXiv:2309.17425}, |
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year={2023} |
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} |
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``` |
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