Downloads · 30 days
273
3% of all-time downloads
QA-CLIP-ViT-B-16
TencentARC/QA-CLIP-ViT-B-16
QA-CLIP-ViT-B-16 is a zero-shot image classification model from TencentARC. Use it for the zero-shot image classification task on the model card, and read the license before you ship it in a product. It is set up for transformers. The card lists the license as apache-2.0.
中文说明 | English 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. A…
All-time downloads
10K
Public
Repo size
2.6 GB
Likes
8
Public
Repo makeup
Click a slice to open those files.
.bin753 MB · 100%
At a glance
- Task
- Zero-Shot Image Classification
- Library
- transformers
- License
- apache-2.0
- Model type
- chinese_clip
- Access
- Public
- Created
- May 12, 2023
- Updated
- May 16, 2023
- SHA
- 8f09600e
Model card
From the Hugging Face model README
Introduction
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. This project is produced by QQ-ARC Joint Lab, Tencent PCG. For more detailed information, please refer to the main page of the QA-CLIP project. We have also open-sourced our code on GitHub, QA-CLIP, and welcome to star! <br><br>
Results
We conducted zero-shot tests on MUGE Retrieval, Flickr30K-CN, and 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:
Flickr30K-CN Zero-shot Retrieval (Official Test Set):
<table border="1" width="120%"> <tr align="center"> <th>Task</th><th colspan="3">Text-to-Image</th><th colspan="3">Image-to-Text</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> </tr> <tr align="center"> <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> </tr> <tr align="center", style="background-color: Honeydew;"> <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> </tr> <tr align="center"> <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> </tr> <tr align="center", style="background-color: Honeydew;"> <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> </tr> <tr align="center"> <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> </tr> <tr align="center"> <td width="120%">AltClip<sub>ViT-L/14</sub></td><td><b>69.7</b></td><td>90.1</td><td><b>94.8</b></td><td>84.8</td><td>97.7</td><td>99.1</td> </tr> <tr align="center", style="background-color: Honeydew;"> <td width="120%">QA-CLIP<sub>ViT-L/14</sub></td><td>69.3</td><td><b>90.3</b></td><td>94.7</td><td><b>85.3</b></td><td><b>97.9</b></td><td><b>99.2</b></td> </tr> </table> <br>MUGE Zero-shot Retrieval (Official Validation Set):
<table border="1" width="120%"> <tr align="center"> <th>Task</th><th colspan="3">Text-to-Image</th><th colspan="3">Image-to-Text</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> </tr> <tr align="center"> <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> </tr> <tr align="center", style="background-color: Honeydew;"> <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> </tr> <tr align="center"> <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> </tr> <tr align="center", style="background-color: Honeydew;"> <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> </tr> <tr align="center"> <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> </tr> <tr align="center"> <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> </tr> <tr align="center", style="background-color: Honeydew;"> <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> </tr> </table> <br>COCO-CN Zero-shot Retrieval (Official Test Set):
<table border="1" width="120%"> <tr align="center"> <th>Task</th><th colspan="3">Text-to-Image</th><th colspan="3">Image-to-Text</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> </tr> <tr align="center"> <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> </tr> <tr align="center", style="background-color: Honeydew;"> <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> </tr> <tr align="center"> <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> </tr> <tr align="center", style="background-color: Honeydew;"> <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> </tr> <tr align="center"> <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> </tr> <tr align="center"> <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> </tr> <tr align="center", style="background-color: Honeydew;"> <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> </tr> </table> <br>Zero-shot Image Classification on ImageNet:
<table border="1" width="120%"> <tr align="center"> <th>Task</th><th colspan="1">ImageNet</th> </tr> <tr align="center"> <td width="120%">CN-CLIP<sub>RN50</sub></td><td>33.5</td> </tr> <tr align="center", style="background-color: Honeydew;"> <td width="120%">QA-CLIP<sub>RN50</sub></td><td><b>35.5</b></td> </tr> <tr align="center"> <td width="120%">CN-CLIP<sub>ViT-B/16</sub></td><td>48.4</td> </tr> <tr align="center", style="background-color: Honeydew;"> <td width="120%">QA-CLIP<sub>ViT-B/16</sub></td><td><b>49.7</b></td> </tr> <tr align="center"> <td width="120%">CN-CLIP<sub>ViT-L/14</sub></td><td>54.7</td> </tr> <tr align="center", style="background-color: Honeydew;"> <td width="120%">QA-CLIP<sub>ViT-L/14</sub></td><td><b>55.8</b></td> </tr> </table> <br><br><br>
Getting Started
Inference Code
Inference code example：
from PIL import Image
import requests
from transformers import ChineseCLIPProcessor, ChineseCLIPModel
model = ChineseCLIPModel.from_pretrained("TencentARC/QA-CLIP-ViT-B-16")
processor = ChineseCLIPProcessor.from_pretrained("TencentARC/QA-CLIP-ViT-B-16")
url = "https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/pokemon.jpeg"
image = Image.open(requests.get(url, stream=True).raw)
# Squirtle, Bulbasaur, Charmander, Pikachu in English
texts = ["杰尼龟", "妙蛙种子", "小火龙", "皮卡丘"]
# compute image feature
inputs = processor(images=image, return_tensors="pt")
image_features = model.get_image_features(**inputs)
image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) # normalize
# compute text features
inputs = processor(text=texts, padding=True, return_tensors="pt")
text_features = model.get_text_features(**inputs)
text_features = text_features / text_features.norm(p=2, dim=-1, keepdim=True) # normalize
# compute image-text similarity scores
inputs = processor(text=texts, images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1)
<br><br>
Acknowledgments
The project code is based on implementation of <b>Chinese-CLIP</b>, and we are very grateful for their outstanding open-source contributions. <br><br>
- Task
- Zero-Shot Image Classification
- Library
- transformers
- Type
- chinese_clip
- License
- apache-2.0
- Created
- May 12, 2023
- Updated
- May 16, 2023