| | --- |
| | license: apple-amlr |
| | license_name: apple-sample-code-license |
| | license_link: LICENSE |
| | --- |
| | |
| | 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). |
| |
|
| | 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-2b |
| | - **Papers:** |
| | - Data Filtering Networks: https://arxiv.org/abs/2309.17425 |
| | - **Examples Seen:** 12.8B |
| |
|
| |
|
| | ## Model Metrics |
| | | Eval Dataset | Metric | |
| | |:-----------------------|---------:| |
| | | ImageNet 1k | 0.81396 | |
| | | Caltech-101 | 0.953141 | |
| | | CIFAR-10 | 0.9836 | |
| | | CIFAR-100 | 0.8835 | |
| | | CLEVR Counts | 0.3338 | |
| | | CLEVR Distance | 0.248733 | |
| | | Country211 | 0.28237 | |
| | | Describable Textures | 0.66117 | |
| | | EuroSAT | 0.646296 | |
| | | FGVC Aircraft | 0.395945 | |
| | | Food-101 | 0.945861 | |
| | | GTSRB | 0.616152 | |
| | | ImageNet Sketch | 0.683311 | |
| | | ImageNet v2 | 0.7453 | |
| | | ImageNet-A | 0.6676 | |
| | | ImageNet-O | 0.3915 | |
| | | ImageNet-R | 0.900033 | |
| | | KITTI Vehicle Distance | 0.201125 | |
| | | MNIST | 0.8468 | |
| | | ObjectNet | 0.739367 | |
| | | Oxford Flowers-102 | 0.865822 | |
| | | Oxford-IIIT Pet | 0.954941 | |
| | | Pascal VOC 2007 | 0.81644 | |
| | | PatchCamelyon | 0.63028 | |
| | | Rendered SST2 | 0.551345 | |
| | | RESISC45 | 0.733175 | |
| | | Stanford Cars | 0.947146 | |
| | | STL-10 | 0.976625 | |
| | | SUN397 | 0.754565 | |
| | | SVHN | 0.653503 | |
| | | Flickr | 0.8244 | |
| | | MSCOCO | 0.570363 | |
| | | WinoGAViL | 0.551645 | |
| | | iWildCam | 0.18877 | |
| | | Camelyon17 | 0.626179 | |
| | | FMoW | 0.222137 | |
| | | Dollar Street | 0.688084 | |
| | | GeoDE | 0.91023 | |
| | | **Average** | **0.668558** | |
| |
|
| | ## 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-L-14') |
| | tokenizer = get_tokenizer('ViT-L-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} |
| | } |
| | |
| | ``` |
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