Hugging Face's logo

TencentARC
/
QA-CLIP

Zero-Shot Classification
Model card Files Community
1
QA-CLIP
File size: 12,314 Bytes 
f76d30f
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
2e97c5d
f76d30f
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
 
[**中文说明**](README_CN.md) | [**English**](README.md)
# 项目介绍
本项目旨在提供更好的中文CLIP模型。该项目使用的训练数据均为公开可访问的图像URL及相关中文文本描述，总量达到400M。经过筛选后，我们最终使用了100M的数据进行训练。
本项目于QQ-ARC Joint Lab, Tencent PCG完成
<br><br>

# 模型及实验
<span id="model_card"></span>
## 模型规模 & 下载链接
QA-CLIP目前开源3个不同规模，其模型信息和下载方式见下表：

<table border="1" width="100%">
    <tr align="center">
        <th>模型规模</th><th>下载链接</th><th>参数量</th><th>视觉侧骨架</th><th>视觉侧参数量</th><th>文本侧骨架</th><th>文本侧参数量</th><th>分辨率</th>
    </tr>
    <tr align="center">
        <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>
    </tr>
    <tr align="center">
        <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>
    </tr>
    <tr align="center">
        <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>
    </tr>
</table>
<br>

## 实验结果
针对图文检索任务，我们在[MUGE Retrieval](https://tianchi.aliyun.com/muge)、[Flickr30K-CN](https://github.com/li-xirong/cross-lingual-cap)和[COCO-CN](https://github.com/li-xirong/coco-cn)上进行了zero-shot测试。
针对图像零样本分类任务，我们在ImageNet数据集上进行了测试。测试结果见下表：


**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">
        <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">
        <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>94.8</td><td>84.8</td><td>97.7</td><td>99.1</td>
    </tr>
	<tr align="center">
        <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>
    </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">
        <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">
        <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">
        <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">
        <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">
        <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">
        <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">
        <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">
        <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">
        <td width="120%">QA-CLIP<sub>ViT-L/14</sub></td><td><b>55.8</b></td>
    </tr>
</table>
<br>

<br><br>


# 使用教程
## 安装要求
环境配置要求:

* python >= 3.6.4
* pytorch >= 1.8.0 (with torchvision >= 0.9.0)
* CUDA Version >= 10.2

安装本项目所需库
```bash
cd /yourpath/QA-CLIP-main
pip install -r requirements.txt
```

## 推理代码
```bash
export PYTHONPATH=/yourpath/QA-CLIP-main
```
推理代码示例：
```python
import torch 
from PIL import Image

import clip as clip
from clip import load_from_name, available_models
print("Available models:", available_models())  
# Available models: ['ViT-B-16', 'ViT-L-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)
    # 对特征进行归一化，请使用归一化后的图文特征用于下游任务
    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)
```
<br><br>

## 预测及评估

### 图文检索测试数据集下载
<b>[Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)</b>项目中已经预处理好测试集，这是他们提供的下载链接：

MUGE数据：[下载链接](https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/MUGE.zip)

Flickr30K-CN数据：[下载链接](https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/Flickr30k-CN.zip)

另外[COCO-CN](https://github.com/li-xirong/coco-cn)数据的获取需要向原作者进行申请
### ImageNet数据集下载
原始数据请自行下载，[中文标签](http://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/ImageNet-1K/label_cn.txt)和[英文标签](http://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/ImageNet-1K/label.txt)同样由<b>[Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)</b>项目提供
### 图文检索评估
图文检索评估代码可以参考如下：
```bash
split=test # 指定计算valid或test集特征
resume=your_ckp_path
DATAPATH=your_DATAPATH
dataset_name=Flickr30k-CN
# dataset_name=MUGE

python -u eval/extract_features.py \
    --extract-image-feats \
    --extract-text-feats \
    --image-data="${DATAPATH}/datasets/${dataset_name}/lmdb/${split}/imgs" \
    --text-data="${DATAPATH}/datasets/${dataset_name}/${split}_texts.jsonl" \
    --img-batch-size=32 \
    --text-batch-size=32 \
    --context-length=52 \
    --resume=${resume} \
    --vision-model=ViT-B-16 \
    --text-model=RoBERTa-wwm-ext-base-chinese

python -u eval/make_topk_predictions.py \
    --image-feats="${DATAPATH}/datasets/${dataset_name}/${split}_imgs.img_feat.jsonl" \
    --text-feats="${DATAPATH}/datasets/${dataset_name}/${split}_texts.txt_feat.jsonl" \
    --top-k=10 \
    --eval-batch-size=32768 \
    --output="${DATAPATH}/datasets/${dataset_name}/${split}_predictions.jsonl"

python -u eval/make_topk_predictions_tr.py \
    --image-feats="${DATAPATH}/datasets/${dataset_name}/${split}_imgs.img_feat.jsonl" \
    --text-feats="${DATAPATH}/datasets/${dataset_name}/${split}_texts.txt_feat.jsonl" \
    --top-k=10 \
    --eval-batch-size=32768 \
    --output="${DATAPATH}/datasets/${dataset_name}/${split}_tr_predictions.jsonl"

python eval/evaluation.py \
    ${DATAPATH}/datasets/${dataset_name}/${split}_texts.jsonl \
    ${DATAPATH}/datasets/${dataset_name}/${split}_predictions.jsonl \
    ${DATAPATH}/datasets/${dataset_name}/output1.json
cat  ${DATAPATH}/datasets/${dataset_name}/output1.json

python eval/transform_ir_annotation_to_tr.py \
    --input ${DATAPATH}/datasets/${dataset_name}/${split}_texts.jsonl

python eval/evaluation_tr.py \
    ${DATAPATH}/datasets/${dataset_name}/${split}_texts.tr.jsonl \
    ${DATAPATH}/datasets/${dataset_name}/${split}_tr_predictions.jsonl \
    ${DATAPATH}/datasets/${dataset_name}/output2.json
cat ${DATAPATH}/datasets/${dataset_name}/output2.json
```

### ImageNet零样本分类
ImageNet零样本分类的代码参考如下
```bash
bash scripts/zeroshot_eval.sh 0 \
    ${DATAPATH} imagenet \
    ViT-B-16 RoBERTa-wwm-ext-base-chinese \
    ./pretrained_weights/QA-CLIP-base.pt
```
<br><br>
# 致谢
项目代码基于<b>[Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP)</b>实现，非常感谢他们优秀的开源工作。
<br><br>