Update README.md

README.md

@@ -3,3 +3,112 @@ license: other
 license_name: apple-sample-code-license
 license_link: LICENSE
 ---
+---
+license: other
+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 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 
+| 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}
+}
+
+```
+