Upload of original files

LICENSE

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+Disclaimer: IMPORTANT: This Apple Machine Learning Research Model is
+specifically developed and released by Apple Inc. ("Apple") for the sole purpose
+of scientific research of artificial intelligence and machine-learning
+technology. “Apple Machine Learning Research Model” means the model, including
+but not limited to algorithms, formulas, trained model weights, parameters,
+configurations, checkpoints, and any related materials (including
+documentation).
+
+This Apple Machine Learning Research Model is provided to You by
+Apple in consideration of your agreement to the following terms, and your use,
+modification, creation of Model Derivatives, and or redistribution of the Apple
+Machine Learning Research Model constitutes acceptance of this Agreement. If You
+do not agree with these terms, please do not use, modify, create Model
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+
+* License Scope: In consideration of your agreement to abide by the following
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+
+* Distribution of Apple Machine Learning Research Model and Model Derivatives:
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+* Term and Termination: The term of this Agreement will begin upon your
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+
+* Disclaimer and Limitation of Liability: This Apple Machine Learning Research
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+
+Copyright (C) 2025 Apple Inc. All Rights Reserved.

README.md

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+---
+license: apple-amlr
+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 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).
+
+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  
+## Model Metrics 
+
+| Eval Dataset                |   Metric |
+|:-----------------------|---------:|
+| ImageNet 1k            | 0.8344   |
+| Caltech-101            | 0.954935 |
+| CIFAR-10               | 0.9878   |
+| CIFAR-100              | 0.9051   |
+| CLEVR Counts           | 0.2966   |
+| CLEVR Distance         | 0.2124   |
+| Country211             | 0.343981 |
+| Describable Textures   | 0.706383 |
+| EuroSAT                | 0.654815 |
+| FGVC Aircraft          | 0.714055 |
+| Food-101               | 0.956792 |
+| GTSRB                  | 0.677514 |
+| ImageNet Sketch        | 0.727308 |
+| ImageNet v2            | 0.773    |
+| ImageNet-A             | 0.6988   |
+| ImageNet-O             | 0.381    |
+| ImageNet-R             | 0.929367 |
+| KITTI Vehicle Distance | 0.336146 |
+| MNIST                  | 0.8579   |
+| ObjectNet              | 0.765156 |
+| Oxford Flowers-102     | 0.899534 |
+| Oxford-IIIT Pet        | 0.965515 |
+| Pascal VOC 2007        | 0.818309 |
+| PatchCamelyon          | 0.653625 |
+| Rendered SST2          | 0.546403 |
+| RESISC45               | 0.750476 |
+| Stanford Cars          | 0.957592 |
+| STL-10                 | 0.989    |
+| SUN397                 | 0.769149 |
+| SVHN                   | 0.676168 |
+| Flickr                 | 0.8645   |
+| MSCOCO                 | 0.631112 |
+| WinoGAViL              | 0.556329 |
+| iWildCam               | 0.205549 |
+| Camelyon17             | 0.705034 |
+| FMoW                   | 0.207482 |
+| Dollar Street          | 0.699766 |
+| GeoDE                  | 0.928184 |
+| **Average**                | **0.698347** |
+## 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')
+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}
+}
+
+```
+

config.json

@@ -0,0 +1,165 @@
+{
+  "_commit_hash": null,
+  "architectures": [
+    "CLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "clip",
+  "projection_dim": 1024,
+  "text_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
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+    "diversity_penalty": 0.0,
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+    "encoder_no_repeat_ngram_size": 0,
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+    "exponential_decay_length_penalty": null,
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+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "max_position_embeddings": 77,
+    "min_length": 0,
+    "model_type": "clip_text_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
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+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": 49408,
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+    "projection_dim": 512,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
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+    "return_dict": true,
+    "return_dict_in_generate": false,
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+    "temperature": 1.0,
+    "tf_legacy_loss": false,
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+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.27.1",
+    "typical_p": 1.0,
+    "use_bfloat16": false,
+    "vocab_size": 49409
+  },
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
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+    "architectures": null,
+    "attention_dropout": 0.0,
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+    "diversity_penalty": 0.0,
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+    "hidden_act": "quick_gelu",
+    "hidden_size": 1280,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 5120,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "clip_vision_model",
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+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
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+    "num_channels": 3,
+    "num_hidden_layers": 32,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
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+    "sep_token_id": null,
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+    "temperature": 1.0,
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+    "tie_word_embeddings": true,
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+    "top_k": 50,
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+    "torchscript": false,
+    "transformers_version": "4.27.1",
+    "typical_p": 1.0,
+    "use_bfloat16": false
+  }
+}

eval_results.jsonl

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+{"key": "imagenet1k", "dataset": "ImageNet 1k", "metrics": {"acc1": 0.8344, "acc5": 0.97192, "mean_per_class_recall": 0.8343200000000001, "main_metric": 0.8344}}
+{"key": "vtab/caltech101", "dataset": "Caltech-101", "metrics": {"acc1": 0.8606409202958094, "acc5": 0.9426458504519309, "mean_per_class_recall": 0.9549354689024778, "main_metric": 0.9549354689024778}}
+{"key": "cifar10", "dataset": "CIFAR-10", "metrics": {"acc1": 0.9878, "acc5": 0.9999, "mean_per_class_recall": 0.9877999999999998, "main_metric": 0.9878}}
+{"key": "vtab/cifar100", "dataset": "CIFAR-100", "metrics": {"acc1": 0.9051, "acc5": 0.9889, "mean_per_class_recall": 0.9051, "main_metric": 0.9051}}
+{"key": "vtab/clevr_count_all", "dataset": "CLEVR Counts", "metrics": {"acc1": 0.2966, "acc5": 0.9220666666666667, "mean_per_class_recall": 0.2981595675798421, "main_metric": 0.2966}}
+{"key": "vtab/clevr_closest_object_distance", "dataset": "CLEVR Distance", "metrics": {"acc1": 0.2124, "acc5": 0.9186666666666666, "mean_per_class_recall": 0.15698927434039325, "main_metric": 0.2124}}
+{"key": "country211", "dataset": "Country211", "metrics": {"acc1": 0.34398104265402846, "acc5": 0.5876303317535545, "mean_per_class_recall": 0.3439810426540285, "main_metric": 0.34398104265402846}}
+{"key": "vtab/dtd", "dataset": "Describable Textures", "metrics": {"acc1": 0.7063829787234043, "acc5": 0.9446808510638298, "mean_per_class_recall": 0.7063829787234043, "main_metric": 0.7063829787234043}}
+{"key": "vtab/eurosat", "dataset": "EuroSAT", "metrics": {"acc1": 0.6548148148148148, "acc5": 0.9825925925925926, "mean_per_class_recall": 0.6619039664988393, "main_metric": 0.6548148148148148}}
+{"key": "fgvc_aircraft", "dataset": "FGVC Aircraft", "metrics": {"acc1": 0.7146714671467147, "acc5": 0.9801980198019802, "mean_per_class_recall": 0.714055258467023, "main_metric": 0.714055258467023}}
+{"key": "food101", "dataset": "Food-101", "metrics": {"acc1": 0.9567920792079208, "acc5": 0.995960396039604, "mean_per_class_recall": 0.9567920792079208, "main_metric": 0.9567920792079208}}
+{"key": "gtsrb", "dataset": "GTSRB", "metrics": {"acc1": 0.677513855898654, "acc5": 0.8931908155186065, "mean_per_class_recall": 0.674850299982757, "main_metric": 0.677513855898654}}
+{"key": "imagenet_sketch", "dataset": "ImageNet Sketch", "metrics": {"acc1": 0.7273084556583937, "acc5": 0.9104914618090354, "mean_per_class_recall": 0.7275686274509803, "main_metric": 0.7273084556583937}}
+{"key": "imagenetv2", "dataset": "ImageNet v2", "metrics": {"acc1": 0.773, "acc5": 0.9405, "mean_per_class_recall": 0.7735, "main_metric": 0.773}}
+{"key": "imagenet-a", "dataset": "ImageNet-A", "metrics": {"acc1": 0.6988, "acc5": 0.8973333333333333, "mean_per_class_recall": 0.6860956321859686, "main_metric": 0.6988}}
+{"key": "imagenet-o", "dataset": "ImageNet-O", "metrics": {"acc1": 0.381, "acc5": 0.734, "mean_per_class_recall": 0.39689795722852994, "main_metric": 0.381}}
+{"key": "imagenet-r", "dataset": "ImageNet-R", "metrics": {"acc1": 0.9293666666666667, "acc5": 0.9822666666666666, "mean_per_class_recall": 0.921225710813732, "main_metric": 0.9293666666666667}}
+{"key": "vtab/kitti_closest_vehicle_distance", "dataset": "KITTI Vehicle Distance", "metrics": {"acc1": 0.3361462728551336, "acc5": null, "mean_per_class_recall": 0.42655862269565553, "main_metric": 0.3361462728551336}}
+{"key": "mnist", "dataset": "MNIST", "metrics": {"acc1": 0.8579, "acc5": 0.9842, "mean_per_class_recall": 0.8580588283530659, "main_metric": 0.8579}}
+{"key": "objectnet", "dataset": "ObjectNet", "metrics": {"acc1": 0.6812749003984063, "acc5": 0.8573812856681382, "mean_per_class_recall": 0.6695348024584162, "main_metric": 0.6812749003984063}}
+{"key": "vtab/flowers", "dataset": "Oxford Flowers-102", "metrics": {"acc1": 0.9234021792161327, "acc5": 0.9829240526914945, "mean_per_class_recall": 0.8995336494286063, "main_metric": 0.8995336494286063}}
+{"key": "vtab/pets", "dataset": "Oxford-IIIT Pet", "metrics": {"acc1": 0.9656582174979559, "acc5": 0.9991823385118561, "mean_per_class_recall": 0.9655148933085753, "main_metric": 0.9655148933085753}}
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+{"key": "vtab/pcam", "dataset": "PatchCamelyon", "metrics": {"acc1": 0.65362548828125, "acc5": null, "mean_per_class_recall": 0.6535354071230998, "main_metric": 0.65362548828125}}
+{"key": "renderedsst2", "dataset": "Rendered SST2", "metrics": {"acc1": 0.5464030752333883, "acc5": null, "mean_per_class_recall": 0.5456925626773204, "main_metric": 0.5464030752333883}}
+{"key": "vtab/resisc45", "dataset": "RESISC45", "metrics": {"acc1": 0.7504761904761905, "acc5": 0.9503174603174603, "mean_per_class_recall": 0.755682846983173, "main_metric": 0.7504761904761905}}
+{"key": "cars", "dataset": "Stanford Cars", "metrics": {"acc1": 0.957592339261286, "acc5": 0.9996269120756125, "mean_per_class_recall": 0.95787629738828, "main_metric": 0.957592339261286}}
+{"key": "stl10", "dataset": "STL-10", "metrics": {"acc1": 0.989, "acc5": 1.0, "mean_per_class_recall": 0.9890000000000001, "main_metric": 0.989}}
+{"key": "sun397", "dataset": "SUN397", "metrics": {"acc1": 0.7691487209665852, "acc5": 0.9687367820953712, "mean_per_class_recall": 0.7712829083687427, "main_metric": 0.7691487209665852}}
+{"key": "vtab/svhn", "dataset": "SVHN", "metrics": {"acc1": 0.6761677934849416, "acc5": 0.922480024585126, "mean_per_class_recall": 0.6992385112125077, "main_metric": 0.6761677934849416}}
+{"key": "retrieval/flickr_1k_test_image_text_retrieval", "dataset": "Flickr", "metrics": {"image_retrieval_recall@1": 0.8009999990463257, "text_retrieval_recall@1": 0.9279999732971191, "image_retrieval_recall@5": 0.9524000287055969, "text_retrieval_recall@5": 0.9940000176429749, "image_retrieval_recall@10": 0.973800003528595, "text_retrieval_recall@10": 0.9990000128746033, "mean_recall@1": 0.8644999861717224, "main_metric": 0.8644999861717224}}
+{"key": "retrieval/mscoco_2014_5k_test_image_text_retrieval", "dataset": "MSCOCO", "metrics": {"image_retrieval_recall@1": 0.5396241545677185, "text_retrieval_recall@1": 0.722599983215332, "image_retrieval_recall@5": 0.7800479531288147, "text_retrieval_recall@5": 0.902400016784668, "image_retrieval_recall@10": 0.855297863483429, "text_retrieval_recall@10": 0.9449999928474426, "mean_recall@1": 0.6311120688915253, "main_metric": 0.6311120688915253}}
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