Update README.md

README.md

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 license: apache-2.0
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+
+# Chinese-CLIP-Base
+
+## Introduction
+This is the base-version of the Chinese CLIP. 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
+
+## How to use
+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>Wukong<sub>ViT-B</sub></td><td>33.4</td><td>59.3</td><td>69.7</td><td>54.1</td><td>39.2</td><td>66.9</td><td>77.4</td><td>61.2</td>
+	</tr>
+	<tr align="center">
+        <td>R2D2<sub>ViT-B</sub></td><td>-</td><td>-</td><td>-</td><td>-</td><td>47.4</td><td>75.1</td><td>83.5</td><td>68.7</td>
+	</tr>
+	<tr align="center">
+        <td>CN-CLIP<sub>ViT-B</sub></td><td><b>52.1</b></td><td><b>76.7</b></td><td><b>84.4</b></td><td><b>71.1</b></td><td><b>58.4</b></td><td><b>83.6</b></td><td><b>90.0</b></td><td><b>77.4</b></td>
+	</tr>
+</table>
+
+
+### Flickr30K-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>Wukong<sub>ViT-B</sub></td><td>45.7</td><td>73.8</td><td>82.2</td><td>67.6</td><td>89.6</td><td>94.2</td><td>66.2</td><td>88.7</td><td>94.3</td><td>83.9</td><td>97.6</td><td>99.0</td>
+	</tr>
+	<tr align="center">
+        <td>R2D2<sub>ViT-B</sub></td><td>-</td><td>-</td><td>-</td><td>78.3</td><td>94.6</td><td>97.0</td><td>-</td><td>-</td><td>-</td><td>92.6</td><td><b>99.1</b></td><td><b>99.8</b></td>
+	</tr>
+	<tr align="center">
+        <td>CN-CLIP<sub>ViT-B</sub></td><td><b>62.7</b></td><td><b>86.9</b></td><td><b>92.8</b></td><td><b>79.1</b></td><td><b>94.8</b></td><td><b>97.4</b></td><td><b>74.6</b></td><td><b>93.5</b></td><td><b>97.1</b></td><td><b>93.5</b></td><td>99.0</td><td>99.5</td>
+	</tr>
+</table>
+
+
+### 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>Wukong<sub>ViT-B</sub></td><td>49.2</td><td>79.4</td><td>87.9</td><td>67.0</td><td>91.4</td><td>96.7</td><td>48.3</td><td>77.8</td><td>88.8</td><td>65.8</td><td>90.3</td><td>96.6</td>
+	</tr>
+	<tr align="center">
+        <td>R2D2<sub>ViT-B</sub></td><td>-</td><td>-</td><td>-</td><td>75.1</td><td>94.2</td><td>98.1</td><td>-</td><td>-</td><td>-</td><td>76.1</td><td>95.3</td><td>98.5</td>
+	</tr>
+	<tr align="center">
+        <td>CN-CLIP<sub>ViT-B</sub></td><td><b>62.2</b></td><td><b>86.6</b></td><td><b>94.9</b></td><td><b>77.0</b></td><td><b>97.1</b></td><td><b>99.0</b></td><td><b>57.0</b></td><td><b>84.1</b></td><td><b>93.6</b></td><td><b>77.4</b></td><td><b>96.2</b></td><td><b>98.9</b></td>
+	</tr>
+</table>
+<br><br>