This repo contains the codebase of a series of research projects focused on adapting vision-language models like CLIP to downstream datasets via prompt learning:
- Conditional Prompt Learning for Vision-Language Models, in CVPR, 2022.
- Learning to Prompt for Vision-Language Models, IJCV, 2022.
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07.10.2022: Just added to both CoOp and CoCoOp (in their appendices) the results on the newly proposed DOSCO (DOmain Shift in COntext) benchmark, which focuses on contextual domain shift and covers a diverse set of classification problems. (The paper about DOSCO is here and the code for running CoOp/CoCoOp on DOSCO is here.)
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17.09.2022: Call for Papers: IJCV Special Issue on The Promises and Dangers of Large Vision Models.
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16.07.2022: CoOp has been accepted to IJCV for publication!
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10.06.2022: Our latest work, Neural Prompt Search, has just been released on arxiv. It provides a novel perspective for fine-tuning large vision models like ViT, so please check it out if you're interested in parameter-efficient fine-tuning/transfer learning. The code is also made public here.
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08.06.2022: If you're looking for the code to draw the few-shot performance curves (like the ones we show in the CoOp's paper), see
draw_curves.py. -
09.04.2022: The pre-trained weights of CoOp on ImageNet are released here.
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11.03.2022: The code of our CVPR'22 paper, "Conditional Prompt Learning for Vision-Language Models," is released.
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15.10.2021: We find that the
best_valmodel and thelast_stepmodel achieve similar performance, so we setTEST.FINAL_MODEL = "last_step"for all datasets to save training time. Why we usedbest_val: the (tiny) validation set was designed for the linear probe approach, which requires extensive tuning for its hyperparameters, so we used thebest_valmodel for CoOp as well for fair comparison (in this way, both approaches have access to the validation set). -
09.10.2021: Important changes are made to Dassl's transforms.py. Please pull the latest commits from https://github.com/KaiyangZhou/Dassl.pytorch and this repo to make sure the code works properly. In particular, 1)
center_cropnow becomes a default transform in testing (applied after resizing the smaller edge to a certain size to keep the image aspect ratio), and 2) for training,Resize(cfg.INPUT.SIZE)is deactivated whenrandom_croporrandom_resized_cropis used. Please read this issue on how these changes might affect the performance. -
18.09.2021: We have fixed an error in Dassl which could cause a training data loader to have zero length (so no training will be performed) when the dataset size is smaller than the batch size (due to
drop_last=True). Please pull the latest commit for Dassl (>=8eecc3c). This error led to lower results for CoOp in EuroSAT's 1- and 2-shot settings (others are all correct). We will update the paper on arxiv to fix this error.
This code is built on top of the awesome toolbox Dassl.pytorch so you need to install the dassl environment first. Simply follow the instructions described here to install dassl as well as PyTorch. After that, run pip install -r requirements.txt under CoOp/ to install a few more packages required by CLIP (this should be done when dassl is activated). Then, you are ready to go.
Follow DATASETS.md to install the datasets.
Click a paper below to see the detailed instructions on how to run the code to reproduce the results.
- Learning to Prompt for Vision-Language Models
- Conditional Prompt Learning for Vision-Language Models
- The pre-trained weights of CoOp (both M=16 & M=4) on ImageNet based on RN50, RN101, ViT-B/16 and ViT-B/32 can be downloaded altogether via this link. The weights can be used to reproduce the results in Table 1 of CoOp's paper (i.e., the results on ImageNet and its four variants with domain shift). To load the weights and run the evaluation code, you will need to specify
--model-dirand--load-epoch(see this script for example). - The raw numerical results can be found at this google drive link.
If you use this code in your research, please kindly cite the following papers
@inproceedings{zhou2022cocoop,
title={Conditional Prompt Learning for Vision-Language Models},
author={Zhou, Kaiyang and Yang, Jingkang and Loy, Chen Change and Liu, Ziwei},
booktitle={IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
@article{zhou2022coop,
title={Learning to Prompt for Vision-Language Models},
author={Zhou, Kaiyang and Yang, Jingkang and Loy, Chen Change and Liu, Ziwei},
journal={International Journal of Computer Vision (IJCV)},
year={2022}
}