--- 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.

# Models and Results ## 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:
ModelCkpParamsVisionParams of VisionTextParams of TextResolution
QA-CLIPRN50Download77MResNet5038MRBT339M224
QA-CLIPViT-B/16Download188MViT-B/1686MRoBERTa-wwm-Base102M224
QA-CLIPViT-L/14Download406MViT-L/14304MRoBERTa-wwm-Base102M224

## 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)**:
TaskText-to-ImageImage-to-Text
MetricR@1R@5R@10R@1R@5R@10
CN-CLIPRN5048.876.084.660.085.992.0
QA-CLIPRN5050.577.486.167.187.993.2
CN-CLIPViT-B/1662.786.992.874.693.597.1
QA-CLIPViT-B/1663.888.093.278.496.198.5
CN-CLIPViT-L/1468.089.794.480.296.698.2
AltClipViT-L/1469.790.194.884.897.799.1
CN-CLIPViT-L/1469.390.394.785.397.999.2

**MUGE Zero-shot Retrieval (Official Validation Set)**:
TaskText-to-ImageImage-to-Text
MetricR@1R@5R@10R@1R@5R@10
CN-CLIPRN5042.668.578.030.056.266.9
QA-CLIPRN5044.069.979.532.459.570.3
CN-CLIPViT-B/1652.176.784.438.765.675.1
QA-CLIPViT-B/1653.277.785.140.768.277.2
CN-CLIPViT-L/1456.479.886.242.669.878.6
AltClipViT-L/1429.649.958.821.442.051.9
QA-CLIPViT-L/1457.481.087.745.573.081.4

**COCO-CN Zero-shot Retrieval (Official Test Set)**:
TaskText-to-ImageImage-to-Text
MetricR@1R@5R@10R@1R@5R@10
CN-CLIPRN5048.181.390.550.981.190.5
QA-CLIPRN5050.182.591.756.785.292.9
CN-CLIPViT-B/1662.287.194.956.384.093.3
QA-CLIPViT-B/1662.987.794.761.587.694.8
CN-CLIPViT-L/1464.988.894.260.684.493.1
AltClipViT-L/1463.587.693.562.688.595.9
QA-CLIPViT-L/1465.790.295.064.588.395.1

**Zero-shot Image Classification on ImageNet**:
TaskImageNet
CN-CLIPRN5033.5
QA-CLIPRN5035.5
CN-CLIPViT-B/1648.4
QA-CLIPViT-B/1649.7
CN-CLIPViT-L/1454.7
QA-CLIPViT-L/1455.8



# 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) ```

## Prediction and Evaluation ### Download Image-text Retrieval Test Dataset In Project [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP), 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 [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP) ### 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 ```

# Acknowledgments The project code is based on implementation of [Chinese-CLIP](https://github.com/OFA-Sys/Chinese-CLIP), and we are very grateful for their outstanding open-source contributions.