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Official implementation for "CLIP-ReID: Exploiting Vision-Language Model for Image Re-identification without Concrete Text Labels" (AAAI 2023)
| Date | Stars |
|---|---|
| 2026-07-31 | 512 |
| 2026-08-02 | 514 |
| 2026-08-06 | 514 |
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## CLIP-ReID: Exploiting Vision-Language Model for Image Re-Identification without Concrete Text Labels [[pdf]](https://arxiv.org/pdf/2211.13977.pdf)
[](https://paperswithcode.com/sota/person-re-identification-on-msmt17?p=clip-reid-exploiting-vision-language-model)
### Pipeline
### Installation
```
conda create -n clipreid python=3.8
conda activate clipreid
conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=10.2 -c pytorch
pip install yacs
pip install timm
pip install scikit-image
pip install tqdm
pip install ftfy
pip install regex
```
### Prepare Dataset
Download the datasets ([Market-1501](https://drive.google.com/file/d/0B8-rUzbwVRk0c054eEozWG9COHM/view), [MSMT17](https://arxiv.org/abs/1711.08565), [DukeMTMC-reID](https://arxiv.org/abs/1609.01775), [Occluded-Duke](https://github.com/lightas/Occluded-DukeMTMC-Dataset), [VehicleID](https://www.pkuml.org/resources/pku-vehicleid.html), [VeRi-776](https://github.com/JDAI-CV/VeRidataset)), and then unzip them to `your_dataset_dir`.
### Training
For example, if you want to run CNN-based CLIP-ReID-baseline for the Market-1501, you need to modify the bottom of configs/person/cnn_base.yml to
```
DATASETS:
NAMES: ('market1501')
ROOT_DIR: ('your_dataset_dir')
OUTPUT_DIR: 'your_output_dir'
```
then run
```
CUDA_VISIBLE_DEVICES=0 python train.py --config_file configs/person/cnn_base.yml
```
if you want to run ViT-based CLIP-ReID for MSMT17, you need to modify the bottom of configs/person/vit_clipreid.yml to
```
DATASETS:
NAMES: ('msmt17')
ROOT_DIR: ('your_dataset_dir')
OUTPUT_DIR: 'your_output_dir'
```
then run
```
CUDA_VISIBLE_DEVICES=0 python train_clipreid.py --config_file configs/person/vit_clipreid.yml
```
if you want to run ViT-based CLIP-ReID+SIE+OLP for MSMT17, run:
```
CUDA_VISIBLE_DEVICES=0 python train_clipreid.py --config_file configs/person/vit_clipreid.yml MODEL.SIE_CAMERA True MODEL.SIE_COE 1.0 MODEL.STRIDE_SIZE '[12, 12]'
```
### Evaluation
For example, if you want to test ViT-based CLIP-ReID for MSMT17
```
CUDA_VISIBLE_DEVICES=0 python test_clipreid.py --config_file configs/person/vit_clipreid.yml TEST.WEIGHT 'your_trained_checkpoints_path/ViT-B-16_60.pth'
```
### Acknowledgement
Codebase from [TransReID](https://github.com/damo-cv/TransReID), [CLIP](https://github.com/openai/CLIP), and [CoOp](https://github.com/KaiyangZhou/CoOp).
The veri776 viewpoint label is from https://github.com/Zhongdao/VehicleReIDKeyPointData.
### Trained models and test logs
| Datasets | MSMT17 | Market | Duke | Occ-Duke | VeRi | VehicleID |
| :-------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: | :----------------------------------------------------------: |
| CNN-baseline | [model](https://drive.google.com/file/d/1s-nZMp-LHG0h4dFwvyP_YNBLTijLcrb0/view?usp=share_link)\|[test](https://drive.google.com/file/d/18EQmBB1-GStmnNvaFNrVbKaaoLIW2Jyz/view?usp=share_link) | [model](https://drive.google.com/file/d/15E4K9eGXMlqOGE1RAgXQjF4MzrFobGim/view?usp=share_link)\|[test](https://drive.google.com/file/d/1CxzntZ8531NWmnp6AUrZh8GCWgunF2XA/view?usp=share_link) | [model](https://drive.google.com/file/d/1f9ZgJZSph7kV7xjhfBVIjFG0hwgeSsSy/viExcerpt of 9,868 characters
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