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--- |
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license: apache-2.0 |
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widget: |
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- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png |
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candidate_labels: 音乐表演, 体育运动 |
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example_title: 猫和狗 |
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--- |
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[**中文说明**](README_CN.md) | [**English**](README.md) |
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# Introduction |
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This project aims to provide a better Chinese CLIP model. The training data used in this project consists of publicly accessible image URLs and related Chinese text descriptions, totaling 400 million. After screening, we ultimately used 100 million data for training. |
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This project is produced by QQ-ARC Joint Lab, Tencent PCG. |
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<br><br> |
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# Models and Results |
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<span id="model_card"></span> |
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## Model Card |
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QA-CLIP currently has three different open-source models of different sizes, and their model information and download links are shown in the table below: |
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<table border="1" width="100%"> |
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<tr align="center"> |
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<th>Model</th><th>Ckp</th><th>Params</th><th>Vision</th><th>Params of Vision</th><th>Text</th><th>Params of Text</th><th>Resolution</th> |
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</tr> |
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<tr align="center"> |
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<td>QA-CLIP<sub>RN50</sub></td><td><a href="https://huggingface.co/TencentARC/QA-CLIP/resolve/main/QA-CLIP-RN50.pt">Download</a></td><td>77M</td><td>ResNet50</td><td>38M</td><td>RBT3</td><td>39M</td><td>224</td> |
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</tr> |
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<tr align="center"> |
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<td>QA-CLIP<sub>ViT-B/16</sub></td><td><a href="https://huggingface.co/TencentARC/QA-CLIP/resolve/main/QA-CLIP-base.pt">Download</a></td><td>188M</td><td>ViT-B/16</td><td>86M</td><td>RoBERTa-wwm-Base</td><td>102M</td><td>224</td> |
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</tr> |
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<tr align="center"> |
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<td>QA-CLIP<sub>ViT-L/14</sub></td><td><a href="https://huggingface.co/TencentARC/QA-CLIP/resolve/main/QA-CLIP-large.pt">Download</a></td><td>406M</td><td>ViT-L/14</td><td>304M</td><td>RoBERTa-wwm-Base</td><td>102M</td><td>224</td> |
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</tr> |
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</table> |
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<br> |
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## Results |
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We conducted zero-shot tests on [MUGE Retrieval](https://tianchi.aliyun.com/muge), [Flickr30K-CN](https://github.com/li-xirong/cross-lingual-cap), and [COCO-CN](https://github.com/li-xirong/coco-cn) datasets for image-text retrieval tasks. For the image zero-shot classification task, we tested on the ImageNet dataset. The test results are shown in the table below: |
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**Flickr30K-CN Zero-shot Retrieval (Official Test Set)**: |
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<table border="1" width="120%"> |
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<tr align="center"> |
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<th>Task</th><th colspan="3">Text-to-Image</th><th colspan="3">Image-to-Text</th> |
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</tr> |
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<tr align="center"> |
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<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> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>RN50</sub></td><td>48.8</td><td>76.0</td><td>84.6</td><td>60.0</td><td>85.9</td><td>92.0</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>RN50</sub></td><td><b>50.5</b></td><td><b>77.4</b></td><td><b>86.1</b></td><td><b>67.1</b></td><td><b>87.9</b></td><td><b>93.2</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-B/16</sub></td><td>62.7</td><td>86.9</td><td>92.8</td><td>74.6</td><td>93.5</td><td>97.1</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>ViT-B/16</sub></td><td><b>63.8</b></td><td><b>88.0</b></td><td><b>93.2</b></td><td><b>78.4</b></td><td><b>96.1</b></td><td><b>98.5</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-L/14</sub></td><td>68.0</td><td>89.7</td><td>94.4</td><td>80.2</td><td>96.6</td><td>98.2</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">AltClip<sub>ViT-L/14</sub></td><td><b>69.7</b></td><td>90.1</td><td>94.8</td><td>84.8</td><td>97.7</td><td>99.1</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-L/14</sub></td><td>69.3</td><td><b>90.3</b></td><td><b>94.7</b></td><td><b>85.3</b></td><td><b>97.9</b></td><td><b>99.2</b></td> |
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</tr> |
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</table> |
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<br> |
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**MUGE Zero-shot Retrieval (Official Validation Set)**: |
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<table border="1" width="120%"> |
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<tr align="center"> |
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<th>Task</th><th colspan="3">Text-to-Image</th><th colspan="3">Image-to-Text</th> |
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</tr> |
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<tr align="center"> |
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<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> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>RN50</sub></td><td>42.6</td><td>68.5</td><td>78.0</td><td>30.0</td><td>56.2</td><td>66.9</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>RN50</sub></td><td><b>44.0</b></td><td><b>69.9</b></td><td><b>79.5</b></td><td><b>32.4</b></td><td><b>59.5</b></td><td><b>70.3</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-B/16</sub></td><td>52.1</td><td>76.7</td><td>84.4</td><td>38.7</td><td>65.6</td><td>75.1</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>ViT-B/16</sub></td><td><b>53.2</b></td><td><b>77.7</b></td><td><b>85.1</b></td><td><b>40.7</b></td><td><b>68.2</b></td><td><b>77.2</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-L/14</sub></td><td>56.4</td><td>79.8</td><td>86.2</td><td>42.6</td><td>69.8</td><td>78.6</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">AltClip<sub>ViT-L/14</sub></td><td>29.6</td><td>49.9</td><td>58.8</td><td>21.4</td><td>42.0</td><td>51.9</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>ViT-L/14</sub></td><td><b>57.4</b></td><td><b>81.0</b></td><td><b>87.7</b></td><td><b>45.5</b></td><td><b>73.0</b></td><td><b>81.4</b></td> |
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</tr> |
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</table> |
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<br> |
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**COCO-CN Zero-shot Retrieval (Official Test Set)**: |
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<table border="1" width="120%"> |
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<tr align="center"> |
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<th>Task</th><th colspan="3">Text-to-Image</th><th colspan="3">Image-to-Text</th> |
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</tr> |
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<tr align="center"> |
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<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> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>RN50</sub></td><td>48.1</td><td>81.3</td><td>90.5</td><td>50.9</td><td>81.1</td><td>90.5</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>RN50</sub></td><td><b>50.1</b></td><td><b>82.5</b></td><td><b>91.7</b></td><td><b>56.7</b></td><td><b>85.2</b></td><td><b>92.9</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-B/16</sub></td><td>62.2</td><td>87.1</td><td>94.9</td><td>56.3</td><td>84.0</td><td>93.3</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>ViT-B/16</sub></td><td><b>62.9</b></td><td><b>87.7</b></td><td><b>94.7</b></td><td><b>61.5</b></td><td><b>87.6</b></td><td><b>94.8</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-L/14</sub></td><td>64.9</td><td>88.8</td><td>94.2</td><td>60.6</td><td>84.4</td><td>93.1</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">AltClip<sub>ViT-L/14</sub></td><td>63.5</td><td>87.6</td><td>93.5</td><td>62.6</td><td><b>88.5</b></td><td><b>95.9</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>ViT-L/14</sub></td><td><b>65.7</b></td><td><b>90.2</b></td><td><b>95.0</b></td><td><b>64.5</b></td><td>88.3</td><td>95.1</td> |
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</tr> |
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</table> |
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<br> |
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**Zero-shot Image Classification on ImageNet**: |
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<table border="1" width="120%"> |
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<tr align="center"> |
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<th>Task</th><th colspan="1">ImageNet</th> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>RN50</sub></td><td>33.5</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>RN50</sub></td><td><b>35.5</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-B/16</sub></td><td>48.4</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>ViT-B/16</sub></td><td><b>49.7</b></td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">CN-CLIP<sub>ViT-L/14</sub></td><td>54.7</td> |
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</tr> |
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<tr align="center"> |
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<td width="120%">QA-CLIP<sub>ViT-L/14</sub></td><td><b>55.8</b></td> |
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</tr> |
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</table> |
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<br> |
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<br><br> |
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# Getting Started |
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## Installation Requirements |
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Environment configuration requirements: |
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* python >= 3.6.4 |
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* pytorch >= 1.8.0 (with torchvision >= 0.9.0) |
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* CUDA Version >= 10.2 |
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Install required packages: |
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```bash |
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cd /yourpath/QA-CLIP-main |
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pip install -r requirements.txt |
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``` |
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## Inference Code |
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Inference code example： |
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```python |
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from PIL import Image |
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import requests |
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from transformers import ChineseCLIPProcessor, ChineseCLIPModel |
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model = ChineseCLIPModel.from_pretrained("TencentARC/QA-CLIP-ViT-B-16") |
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processor = ChineseCLIPProcessor.from_pretrained("TencentARC/QA-CLIP-ViT-B-16") |
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url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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# Squirtle, Bulbasaur, Charmander, Pikachu in English |
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texts = ["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"] |
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# compute image feature |
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inputs = processor(images=image, return_tensors="pt") |
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image_features = model.get_image_features(**inputs) |
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image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) # normalize |
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# compute text features |
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inputs = processor(text=texts, padding=True, return_tensors="pt") |
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text_features = model.get_text_features(**inputs) |
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text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True) # normalize |
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# compute image-text similarity scores |
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inputs = processor(text=texts, images=image, return_tensors="pt", padding=True) |
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outputs = model(**inputs) |
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logits_per_image = outputs.logits_per_image # this is the image-text similarity score |
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probs = logits_per_image.softmax(dim=1) |
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``` |
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<br><br> |
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## Prediction and Evaluation |
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### Download Image-text Retrieval Test Dataset |
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In Project <b>[Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)</b>, the test set has already been preprocessed. Here is the download link they provided: |
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MUGE dataset：[download link](https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/MUGE.zip) |
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Flickr30K-CN dataset：[download link](https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/Flickr30k-CN.zip) |
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Additionally, obtaining the [COCO-CN](https://github.com/li-xirong/coco-cn) dataset requires applying to the original author. |
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### Download ImageNet Dataset |
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Please download the raw data yourself，[Chinese Label](http://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/ImageNet-1K/label_cn.txt) and [English Label](http://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/ImageNet-1K/label.txt) are provided by Project <b>[Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)</b> |
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### Image-text Retrieval Evaluation |
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The image-text retrieval evaluation code can be referred to as follows: |
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```bash |
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split=test # Designate the computation of features for the valid or test set |
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resume=your_ckp_path |
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DATAPATH=your_DATAPATH |
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dataset_name=Flickr30k-CN |
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# dataset_name=MUGE |
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python -u eval/extract_features.py \ |
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--extract-image-feats \ |
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--extract-text-feats \ |
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--image-data="${DATAPATH}/datasets/${dataset_name}/lmdb/${split}/imgs" \ |
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--text-data="${DATAPATH}/datasets/${dataset_name}/${split}_texts.jsonl" \ |
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--img-batch-size=32 \ |
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--text-batch-size=32 \ |
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--context-length=52 \ |
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--resume=${resume} \ |
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--vision-model=ViT-B-16 \ |
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--text-model=RoBERTa-wwm-ext-base-chinese |
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python -u eval/make_topk_predictions.py \ |
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--image-feats="${DATAPATH}/datasets/${dataset_name}/${split}_imgs.img_feat.jsonl" \ |
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--text-feats="${DATAPATH}/datasets/${dataset_name}/${split}_texts.txt_feat.jsonl" \ |
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--top-k=10 \ |
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--eval-batch-size=32768 \ |
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--output="${DATAPATH}/datasets/${dataset_name}/${split}_predictions.jsonl" |
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python -u eval/make_topk_predictions_tr.py \ |
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--image-feats="${DATAPATH}/datasets/${dataset_name}/${split}_imgs.img_feat.jsonl" \ |
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--text-feats="${DATAPATH}/datasets/${dataset_name}/${split}_texts.txt_feat.jsonl" \ |
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--top-k=10 \ |
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--eval-batch-size=32768 \ |
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--output="${DATAPATH}/datasets/${dataset_name}/${split}_tr_predictions.jsonl" |
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python eval/evaluation.py \ |
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${DATAPATH}/datasets/${dataset_name}/${split}_texts.jsonl \ |
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${DATAPATH}/datasets/${dataset_name}/${split}_predictions.jsonl \ |
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${DATAPATH}/datasets/${dataset_name}/output1.json |
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cat ${DATAPATH}/datasets/${dataset_name}/output1.json |
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python eval/transform_ir_annotation_to_tr.py \ |
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--input ${DATAPATH}/datasets/${dataset_name}/${split}_texts.jsonl |
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python eval/evaluation_tr.py \ |
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${DATAPATH}/datasets/${dataset_name}/${split}_texts.tr.jsonl \ |
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${DATAPATH}/datasets/${dataset_name}/${split}_tr_predictions.jsonl \ |
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${DATAPATH}/datasets/${dataset_name}/output2.json |
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cat ${DATAPATH}/datasets/${dataset_name}/output2.json |
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``` |
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### ImageNet Zero-shot Classification |
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The ImageNet zero-shot classification code can be referred to as follows |
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```bash |
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bash scripts/zeroshot_eval.sh 0 \ |
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${DATAPATH} imagenet \ |
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ViT-B-16 RoBERTa-wwm-ext-base-chinese \ |
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./pretrained_weights/QA-CLIP-base.pt |
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``` |
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<br><br> |
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# Acknowledgments |
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The project code is based on implementation of <b>[Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)</b>, and we are very grateful for their outstanding open-source contributions. |
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<br><br> |
