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README.md

@@ -1,3 +1,285 @@
 ---
 license: apache-2.0
+widget:
+- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
+  candidate_labels: 音乐表演, 体育运动
+  example_title: 猫和狗
 ---
+[**中文说明**](README_CN.md) | [**English**](README.md)
+# 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.
+<br><br>
+
+# Models and Results
+<span id="model_card"></span>
+## Model Card
+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:
+<table border="1" width="100%">
+    <tr align="center">
+        <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>
+    </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>
+
+## Results
+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:
+
+**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>
+
+
+# Getting Started
+## Installation Requirements
+Environment configuration requirements:
+
+* python >= 3.6.4
+* pytorch >= 1.8.0 (with torchvision >= 0.9.0)
+* CUDA Version >= 10.2
+
+Install required packages:
+```bash
+cd /yourpath/QA-CLIP-main
+pip install -r requirements.txt
+```
+
+## Inference Code
+```bash
+export PYTHONPATH=/yourpath/QA-CLIP-main
+```
+Inference code example：
+```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)
+    # 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)
+```
+<br><br>
+
+## Prediction and Evaluation
+
+### Download Image-text Retrieval Test Dataset
+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:
+
+MUGE dataset：[download link](https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/MUGE.zip)
+
+Flickr30K-CN dataset：[download link](https://clip-cn-beijing.oss-cn-beijing.aliyuncs.com/datasets/Flickr30k-CN.zip)
+
+Additionally, obtaining the [COCO-CN](https://github.com/li-xirong/coco-cn) dataset requires applying to the original author.
+
+### Download ImageNet Dataset
+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>
+### Image-text Retrieval Evaluation
+The image-text retrieval evaluation code can be referred to as follows:
+```bash
+split=test # Designate the computation of features for the valid or test set
+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 Zero-shot Classification
+The ImageNet zero-shot classification code can be referred to as follows
+```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>
+# Acknowledgments
+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.
+<br><br>

README_CN.md

@@ -0,0 +1,280 @@
+[**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>

config.json

@@ -0,0 +1,120 @@
+{
+  "architectures": [
+    "ChineseCLIPModel"
+  ],
+  "initializer_factor": 1.0,
+  "logit_scale_init_value": 2.6592,
+  "model_type": "chinese_clip",
+  "projection_dim": 768,
+  "text_config": {
+    "architectures": [
+      "ChineseCLIPTextModel"
+    ],
+    "attention_probs_dropout_prob": 0.1,
+    "bos_token_id": 0,
+    "directionality": "bidi",
+    "eos_token_id": 2,
+    "hidden_act": "gelu",
+    "hidden_dropout_prob": 0.1,
+    "hidden_size": 768,
+    "initializer_range": 0.02,
+    "intermediate_size": 3072,
+    "layer_norm_eps": 1e-12,
+    "max_position_embeddings": 512,
+    "model_type": "chinese_clip_text_model",
+    "num_attention_heads": 12,
+    "num_hidden_layers": 12,
+    "output_past": true,
+    "pad_token_id": 0,
+    "pooler_fc_size": 768,
+    "pooler_num_attention_heads": 12,
+    "pooler_num_fc_layers": 3,
+    "pooler_size_per_head": 128,
+    "pooler_type": "first_token_transform",
+    "type_vocab_size": 2,
+    "vocab_size": 21128
+  },
+  "text_config_dict": null,
+  "torch_dtype": "float32",
+  "transformers_version": null,
+  "vision_config": {
+    "_name_or_path": "",
+    "add_cross_attention": false,
+    "architectures": null,
+    "attention_dropout": 0.0,
+    "bad_words_ids": null,
+    "bos_token_id": null,
+    "chunk_size_feed_forward": 0,
+    "cross_attention_hidden_size": null,
+    "decoder_start_token_id": null,
+    "diversity_penalty": 0.0,
+    "do_sample": false,
+    "dropout": 0.0,
+    "early_stopping": false,
+    "encoder_no_repeat_ngram_size": 0,
+    "eos_token_id": null,
+    "finetuning_task": null,
+    "forced_bos_token_id": null,
+    "forced_eos_token_id": null,
+    "hidden_act": "quick_gelu",
+    "hidden_size": 1024,
+    "id2label": {
+      "0": "LABEL_0",
+      "1": "LABEL_1"
+    },
+    "image_size": 224,
+    "initializer_factor": 1.0,
+    "initializer_range": 0.02,
+    "intermediate_size": 4096,
+    "is_decoder": false,
+    "is_encoder_decoder": false,
+    "label2id": {
+      "LABEL_0": 0,
+      "LABEL_1": 1
+    },
+    "layer_norm_eps": 1e-05,
+    "length_penalty": 1.0,
+    "max_length": 20,
+    "min_length": 0,
+    "model_type": "clip_vision_model",
+    "no_repeat_ngram_size": 0,
+    "num_attention_heads": 16,
+    "num_beam_groups": 1,
+    "num_beams": 1,
+    "num_hidden_layers": 24,
+    "num_return_sequences": 1,
+    "output_attentions": false,
+    "output_hidden_states": false,
+    "output_scores": false,
+    "pad_token_id": null,
+    "patch_size": 14,
+    "prefix": null,
+    "problem_type": null,
+    "projection_dim" : 768,
+    "pruned_heads": {},
+    "remove_invalid_values": false,
+    "repetition_penalty": 1.0,
+    "return_dict": true,
+    "return_dict_in_generate": false,
+    "sep_token_id": null,
+    "task_specific_params": null,
+    "temperature": 1.0,
+    "tie_encoder_decoder": false,
+    "tie_word_embeddings": true,
+    "tokenizer_class": null,
+    "top_k": 50,
+    "top_p": 1.0,
+    "torch_dtype": null,
+    "torchscript": false,
+    "transformers_version": "4.16.0.dev0",
+    "use_bfloat16": false
+  },
+  "vision_config_dict": {
+    "hidden_size": 1024,
+    "intermediate_size": 4096,
+    "num_attention_heads": 16,
+    "num_hidden_layers": 24,
+    "patch_size": 14,
+    "projection_dim": 768
+  }
+}

preprocessor_config.json

@@ -0,0 +1,21 @@
+{
+  "do_center_crop": false,
+  "do_normalize": true,
+  "do_resize": true,
+  "feature_extractor_type": "ChineseCLIPFeatureExtractor",
+  "image_mean": [
+    0.48145466,
+    0.4578275,
+    0.40821073
+  ],
+  "image_std": [
+    0.26862954,
+    0.26130258,
+    0.27577711
+  ],
+  "resample": 3,
+  "size": {
+    "height": 224, 
+    "width": 224
+  }
+}

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