Update README.md

README.md

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 ---
+name: DFN5B-CLIP-ViT-H-14-378-SAFETENSORS
+base_model: laion/CLIP-ViT-H-14-laion2B-s32B-b79K
 license: apple-amlr
-license_name: apple-sample-code-license
+pipeline_tag: zero-shot-image-classification
+tags: 
+- clip
+- Apple
+- OpenAI
+tasks:
+ - contrastive image-text
+ - vision
+language: en
+papers:
+- https://arxiv.org/abs/2309.17425
+datasets:
+- CommonPool-12.8B
 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).
+> [!IMPORTANT]
+> Original Model Link : [https://huggingface.co/apple/DFN5B-CLIP-ViT-H-14-378](https://huggingface.co/apple/DFN5B-CLIP-ViT-H-14-378)
+> 
 
-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 <sup>1</sup>              | 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)
+name: DFN5B-CLIP-ViT-H-14-378-SAFETENSORS
+base_model: laion/CLIP-ViT-H-14-laion2B-s32B-b79K
+license: apple-amlr
+pipeline_tag: zero-shot-image-classification
+tags: 
+- clip
+- Apple
+- OpenAI
+tasks:
+ - contrastive image-text
+ - vision
+language: en
+papers:
+- https://arxiv.org/abs/2309.17425
+datasets:
+- CommonPool-12.8B
+license_link: LICENS
 ```
-
-## 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}
-}
-
-```
+# DFN5B-CLIP-ViT-H-14-378-SAFETENSORS
+CLIP ViT-H/14 trained on DFN-5B Data Filtering Network filtered from a 43B uncurated pool from CommonPool-12.8B + 30B public images.