add Foundation ViT doc

docs/zh_CN/models/ImageNet1k/FoundationViT.md

+# Foudation ViT介绍文档
+
+## 目录
+
+1. [功能介绍](#1-功能介绍)
+2. [使用说明](#2-使用说明)
+3. [模型介绍](#3-模型介绍)
+4. [参考文献](#4-参考文献)
+
+## 1. 功能介绍
+
+为支持视觉大模型的使用，PaddleClas提供了各系列视觉大模型的预训练权重以及特征提取功能，可使用该功能得到在大数据上完成预训练的视觉大模型特征。
+
+## 2. 使用说明
+
+以模型`CLIP_base_patch16_224`为例，使用该模型以及对应的预训练权重进行特征提取的代码如下：
+
+```python
+from ppcls.utils import config
+from ppcls.arch import build_model
+import paddle
+pretrained = './paddle_weights/CAE_base_patch16_224.pdparams' # path to pretrained weight
+cfg = {"Arch": {"name": "CLIP_base_patch16_224"}}
+model = build_model(cfg, mode="train")
+model.set_state_dict(paddle.load(pretrained))
+inputs = paddle.randn((1,3,224,224))  # create input
+output = model(inputs)  # the output of model embeding
+```
+
+## 3. 模型介绍
+
+目前支持的视觉大模型以及预训练权重如下：
+
+|  系列  |           模型           | 模型大小 | embedding_size |                   预训练数据集                   |
+| :----: | :----------------------: | :------: | :------------: | :----------------------------------------------: |
+|  CLIP  |  CLIP_base_patch16_224   |   85M    |      768       |                       WIT                        |
+|  CLIP  |  CLIP_base_patch32_224   |   87M    |      768       |                       WIT                        |
+|  CLIP  |  CLIP_large_patch14_224  |   302M   |      1024      |                       WIT                        |
+|  CLIP  |  CLIP_large_patch14_336  |   302M   |      1024      |                       WIT                        |
+| BEiTv2 | BEiTv2_base_patch16_224  |   85M    |      768       |                   ImageNet-1k                    |
+| BEiTv2 | BEiTv2_large_patch16_224 |   303M   |      1024      |                   ImageNet-1k                    |
+| MoCoV3 |       MoCoV3_small       |   21M    |      384       |                   ImageNet-1k                    |
+| MoCoV3 |       MoCoV3_base        |   85M    |      768       |                   ImageNet-1k                    |
+|  MAE   |     MAE_base_patch16     |   85M    |      768       |                   ImageNet-1k                    |
+|  MAE   |    MAE_large_patch16     |   303M   |      1024      |                   ImageNet-1k                    |
+|  MAE   |     MAE_huge_patch14     |   630M   |      1280      |                   ImageNet-1k                    |
+|  EVA   |     EVA_huge_patch14     |  1010M   |      1408      | ImageNet-21k, CC12M,   CC2M, Object365,COCO, ADE |
+|  CAE   |   CAE_base_patch16_224   |   85M    |      768       |                   ImageNet-1k                    |
+
+## 4. 参考文献
+
+1. [MoCo v3: An Empirical Study of Training Self-Supervised Vision Transformers](https://arxiv.org/pdf/2104.02057.pdf)
+2. [CLIP: Learning Transferable Visual Models From Natural Language Supervision](https://arxiv.org/abs/2103.00020)
+3. [BEiT v2: Masked Image Modeling with Vector-Quantized Visual Tokenizers](https://arxiv.org/abs/2208.06366)
+4. [CAE: Context Autoencoder for Self-Supervised Representation Learning](https://arxiv.org/abs/2202.03026)
+5. [EVA: EVA: Exploring the Limits of Masked Visual Representation Learning at Scale](https://paperswithcode.com/paper/eva-exploring-the-limits-of-masked-visual)
+6. [MAE: Masked Autoencoders Are Scalable Vision Learners](https://paperswithcode.com/paper/masked-autoencoders-are-scalable-vision)
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