Update README.md

README.md

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 license: apache-2.0
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+
+# Chinese-CLIP-Base
+
+## Introduction
+This is the smallest model of the Chinese CLIP series, ResNet-50 as the image encoder and RBT3 as the text encoder. Chinese CLIP is a simple implementation of CLIP on a large-scale dataset of around 200 million Chinese image-text pairs. For more details, please refer to our technical report https://arxiv.org/abs/2211.01335 and our official github repo https://github.com/OFA-Sys/Chinese-CLIP
+
+## Use with the official API
+We provide a simple code snippet to show how to use the API for Chinese-CLIP. For starters, please install cn_clip:
+```bash
+# to install the latest stable release
+pip install cn_clip
+
+# or install from source code
+cd Chinese-CLIP
+pip install -e .
+```
+After installation, use Chinese CLIP as shown below:
+```python
+import torch
+from PIL import Image
+
+import cn_clip.clip as clip
+from cn_clip.clip import load_from_name, available_models
+print("Available models:", available_models())  
+# Available models: ['ViT-B-16', 'ViT-L-14', 'ViT-L-14-336', 'ViT-H-14', 'RN50']
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model, preprocess = load_from_name("ViT-B-16", device=device, download_root='./')
+model.eval()
+image = preprocess(Image.open("examples/pokemon.jpeg")).unsqueeze(0).to(device)
+text = clip.tokenize(["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"]).to(device)
+
+with torch.no_grad():
+    image_features = model.encode_image(image)
+    text_features = model.encode_text(text)
+    # Normalize the features. Please use the normalized features for downstream tasks.
+    image_features /= image_features.norm(dim=-1, keepdim=True) 
+    text_features /= text_features.norm(dim=-1, keepdim=True)      
+
+    logits_per_image, logits_per_text = model.get_similarity(image, text)
+    probs = logits_per_image.softmax(dim=-1).cpu().numpy()
+
+print("Label probs:", probs)  # [[1.268734e-03 5.436878e-02 6.795761e-04 9.436829e-01]]
+```
+
+However, if you are not satisfied with only using the API, feel free to check our github repo https://github.com/OFA-Sys/Chinese-CLIP for more details about training and inference. 
+<br><br>
+
+## Results
+**MUGE Text-to-Image Retrieval**:
+<table border="1" width="100%">
+    <tr align="center">
+        <th>Setup</th><th colspan="4">Zero-shot</th><th colspan="4">Finetune</th>
+    </tr>
+    <tr align="center">
+        <td>Metric</td><td>R@1</td><td>R@5</td><td>R@10</td><td>MR</td><td>R@1</td><td>R@5</td><td>R@10</td><td>MR</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">Wukong</td><td>42.7</td><td>69.0</td><td>78.0</td><td>63.2</td><td>52.7</td><td>77.9</td><td>85.6</td><td>72.1</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">R2D2</td><td>49.5</td><td>75.7</td><td>83.2</td><td>69.5</td><td>60.1</td><td>82.9</td><td>89.4</td><td>77.5</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">CN-CLIP</td><td>63.0</td><td>84.1</td><td>89.2</td><td>78.8</td><td>68.9</td><td>88.7</td><td>93.1</td><td>83.6</td>
+    </tr>
+</table>
+<br>
+
+**Flickr30K-CN Retrieval**:
+<table border="1" width="120%">
+	<tr align="center">
+        <th>Task</th><th colspan="6">Text-to-Image</th><th colspan="6">Image-to-Text</th>
+    </tr>
+    <tr align="center">
+        <th>Setup</th><th colspan="3">Zero-shot</th><th colspan="3">Finetune</th><th colspan="3">Zero-shot</th><th colspan="3">Finetune</th>
+    </tr>
+    <tr align="center">
+        <td>Metric</td><td>R@1</td><td>R@5</td><td>R@10</td><td>R@1</td><td>R@5</td><td>R@10</td><td>R@1</td><td>R@5</td><td>R@10</td><td>R@1</td><td>R@5</td><td>R@10</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">Wukong</td><td>51.7</td><td>78.9</td><td>86.3</td><td>77.4</td><td>94.5</td><td>97.0</td><td>76.1</td><td>94.8</td><td>97.5</td><td>92.7</td><td>99.1</td><td>99.6</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">R2D2</td><td>60.9</td><td>86.8</td><td>92.7</td><td>84.4</td><td>96.7</td><td>98.4</td><td>77.6</td><td>96.7</td><td>98.9</td><td>95.6</td><td>99.8</td><td>100.0</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">CN-CLIP</td><td>71.2</td><td>91.4</td><td>95.5</td><td>83.8</td><td>96.9</td><td>98.6</td><td>81.6</td><td>97.5</td><td>98.8</td><td>95.3</td><td>99.7</td><td>100.0</td>
+    </tr>
+</table>
+<br>
+
+**COCO-CN Retrieval**:
+<table border="1" width="100%">
+	<tr align="center">
+        <th>Task</th><th colspan="6">Text-to-Image</th><th colspan="6">Image-to-Text</th>
+    </tr>
+    <tr align="center">
+        <th>Setup</th><th colspan="3">Zero-shot</th><th colspan="3">Finetune</th><th colspan="3">Zero-shot</th><th colspan="3">Finetune</th>
+    </tr>
+    <tr align="center">
+        <td>Metric</td><td>R@1</td><td>R@5</td><td>R@10</td><td>R@1</td><td>R@5</td><td>R@10</td><td>R@1</td><td>R@5</td><td>R@10</td><td>R@1</td><td>R@5</td><td>R@10</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">Wukong</td><td>53.4</td><td>80.2</td><td>90.1</td><td>74.0</td><td>94.4</td><td>98.1</td><td>55.2</td><td>81.0</td><td>90.6</td><td>73.3</td><td>94.0</td><td>98.0</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">R2D2</td><td>56.4</td><td>85.0</td><td>93.1</td><td>79.1</td><td>96.5</td><td>98.9</td><td>63.3</td><td>89.3</td><td>95.7</td><td>79.3</td><td>97.1</td><td>98.7</td>
+    </tr>
+	<tr align="center">
+        <td width="120%">CN-CLIP</td><td>69.2</td><td>89.9</td><td>96.1</td><td>81.5</td><td>96.9</td><td>99.1</td><td>63.0</td><td>86.6</td><td>92.9</td><td>83.5</td><td>97.3</td><td>99.2</td>
+    </tr>
+</table>
+<br>
+
+**Zero-shot Image Classification**:
+<table border="1" width="100%">
+	<tr align="center">
+        <th>Task</th><th>CIFAR10</th><th>CIFAR100</th><th>DTD</th><th>EuroSAT</th><th>FER</th><th>FGVC</th><th>KITTI</th><th>MNIST</th><th>PC</th><th>VOC</th>
+    </tr>
+	<tr align="center">
+        <td width="150%">GIT</td><td>88.5</td><td>61.1</td><td>42.9</td><td>43.4</td><td>41.4</td><td>6.7</td><td>22.1</td><td>68.9</td><td>50.0</td><td>80.2</td>
+    </tr>
+    	<tr align="center">
+        <td width="150%">ALIGN</td><td>94.9</td><td>76.8</td><td>66.1</td><td>52.1</td><td>50.8</td><td>25.0</td><td>41.2</td><td>74.0</td><td>55.2</td><td>83.0</td>
+    </tr>
+	<tr align="center">
+        <td width="150%">CLIP</td><td>94.9</td><td>77.0</td><td>56.0</td><td>63.0</td><td>48.3</td><td>33.3</td><td>11.5</td><td>79.0</td><td>62.3</td><td>84.0</td>
+    </tr>
+    	<tr align="center">
+        <td width="150%">Wukong</td><td>95.4</td><td>77.1</td><td>40.9</td><td>50.3</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td>
+    </tr>
+    	<tr align="center">
+        <td width="150%">CN-CLIP</td><td>96.0</td><td>79.7</td><td>51.2</td><td>52.0</td><td>55.1</td><td>26.2</td><td>49.9</td><td>79.4</td><td>63.5</td><td>84.9</td>
+    </tr>
+</table>
+<br><br>
+
+## Citation
+If you find Chinese CLIP helpful, feel free to cite our paper. Thanks for your support!
+
+```
+@article{chinese-clip,
+  title={Chinese CLIP: Contrastive Vision-Language Pretraining in Chinese},
+  author={Yang, An and Pan, Junshu and Lin, Junyang and Men, Rui and Zhang, Yichang and Zhou, Jingren and Zhou, Chang},
+  journal={arXiv preprint arXiv:2211.01335},
+  year={2022}
+}
+```
+<br>