Update README.md

README.md

@@ -3,3 +3,105 @@ 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 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 
+
+| Eval Dataset                |   Metric |
+|:-----------------------|---------:|
+| ImageNet 1k            | 0.84206  |
+| Caltech-101            | 0.951389 |
+| CIFAR-10               | 0.9879   |
+| CIFAR-100              | 0.9041   |
+| CLEVR Counts           | 0.3424   |
+| CLEVR Distance         | 0.214933 |
+| Country211             | 0.3591   |
+| Describable Textures   | 0.707979 |
+| EuroSAT                | 0.608333 |
+| FGVC Aircraft          | 0.657362 |
+| Food-101               | 0.962099 |
+| GTSRB                  | 0.681077 |
+| ImageNet Sketch        | 0.72214  |
+| ImageNet v2            | 0.7787   |
+| ImageNet-A             | 0.802    |
+| ImageNet-O             | 0.3945   |
+| ImageNet-R             | 0.929    |
+| KITTI Vehicle Distance | 0.40225  |
+| MNIST                  | 0.8372   |
+| ObjectNet              | 0.796867 |
+| Oxford Flowers-102     | 0.896257 |
+| Oxford-IIIT Pet        | 0.968432 |
+| Pascal VOC 2007        | 0.7914   |
+| PatchCamelyon          | 0.695953 |
+| Rendered SST2          | 0.566722 |
+| RESISC45               | 0.755079 |
+| Stanford Cars          | 0.95809  |
+| STL-10                 | 0.991125 |
+| SUN397                 | 0.768257 |
+| SVHN                   | 0.671251 |
+| Flickr                 | 0.8663   |
+| MSCOCO                 | 0.636489 |
+| WinoGAViL              | 0.570759 |
+| iWildCam               | 0.215716 |
+| Camelyon17             | 0.711536 |
+| FMoW                   | 0.209024 |
+| Dollar Street          | 0.711449 |
+| GeoDE                  | 0.921503 |
+| **Average**                | **0.704914** |
+
+## 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}
+}
+
+```