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README.md

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+---
+license: apache-2.0
+---
+
+# Vision Transformer
+
+Vision Transformer (ViT) model pre-trained on ImageNet-21k (14 million images, 21,843 classes) at resolution 224x224. It was introduced in the paper [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/abs/2010.11929) and further enhanced in the follow-up paper [How to train your ViT? Data, Augmentation, and Regularization in Vision Transformers](https://arxiv.org/abs/2106.10270).
+The weights were converted from the `B_16-i21k-300ep-lr_0.001-aug_medium1-wd_0.1-do_0.0-sd_0.0.npz` file in [GCS buckets](https://console.cloud.google.com/storage/browser/vit_models/augreg/) presented in the [original repository](https://github.com/google-research/vision_transformer).

config.json

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+{
+  "_name_or_path": "cs-giung/vit-base-patch16-imagenet21k-augreg",
+  "architectures": [
+    "ViTForImageClassification"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.0,
+  "hidden_size": 768,
+  "image_size": 224,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-06,
+  "model_type": "vit",
+  "num_attention_heads": 12,
+  "num_channels": 3,
+  "num_hidden_layers": 12,
+  "num_labels": 21843,
+  "patch_size": 16,
+  "qkv_bias": true,
+  "transformers_version": "4.41.1"
+}

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model.safetensors

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