Created by Yongming Rao*, Wenliang Zhao*, Guangyi Chen, Yansong Tang, Zheng Zhu, Guan Huang, Jie Zhou, and Jiwen Lu.
This repository contains PyTorch implementation for DenseCLIP (CVPR 2022).
DenseCLIP is a new framework for dense prediction by implicitly and explicitly leveraging the pre-trained knowledge from CLIP. Specifically, we convert the original image-text matching problem in CLIP to a pixel-text matching problem and use the pixel-text score maps to guide the learning of dense prediction models. By further using the contextual information from the image to prompt the language model, we are able to facilitate our model to better exploit the pre-trained knowledge. Our method is model-agnostic, which can be applied to arbitrary dense prediction systems and various pre-trained visual backbones including both CLIP models and ImageNet pre-trained models.
Our code is based on mmsegmentation and mmdetection.
To use our code, please first install the mmcv-full
and mmseg
/mmdet
following the official guidelines (mmseg
, mmdet
) and prepare the datasets accordingly.
Download the pre-trained CLIP models (RN50.pt
, RN101.pt
, VIT-B-16.pt
) and save them to the pretrained
folder. The download links can be found in the official CLIP repo.
We provide DenseCLIP models for Semantic FPN framework.
Model | FLOPs (G) | Params (M) | mIoU(SS) | mIoU(MS) | config | url |
---|---|---|---|---|---|---|
RN50-CLIP | 248.8 | 31.0 | 39.6 | 41.6 | config | - |
RN50-DenseCLIP | 269.2 | 50.3 | 43.5 | 44.7 | config | Tsinghua Cloud |
RN101-CLIP | 326.6 | 50.0 | 42.7 | 44.3 | config | - |
RN101-DenseCLIP | 346.3 | 67.8 | 45.1 | 46.5 | config | Tsinghua Cloud |
ViT-B-CLIP | 1037.4 | 100.8 | 49.4 | 50.3 | config | - |
ViT-B-DenseCLIP | 1043.1 | 105.3 | 50.6 | 51.3 | config | Tsinghua Cloud |
To train the DenseCLIP model based on CLIP ResNet-50, run:
bash dist_train.sh configs/denseclip_fpn_res50_512x512_80k.py 8
To evaluate the performance with multi-scale testing, run:
bash dist_test.sh configs/denseclip_fpn_res50_512x512_80k.py /path/to/checkpoint 8 --eval mIoU --aug-test
To better measure the complexity of the models, we provide a tool based on fvcore
to accurately compute the FLOPs of torch.einsum
and other operations:
python get_flops.py /path/to/config --fvcore
You can also remove the --fvcore
flag to obtain the FLOPs measured by mmcv
for comparisons.
We provide models for both RetinaNet and Mask-RCNN framework.
Model | FLOPs (G) | Params (M) | box AP | config | url |
---|---|---|---|---|---|
RN50-CLIP | 265 | 38 | 36.9 | config | - |
RN50-DenseCLIP | 285 | 60 | 37.8 | config | Tsinghua Cloud |
RN101-CLIP | 341 | 57 | 40.5 | config | - |
RN101-DenseCLIP | 360 | 78 | 41.1 | config | Tsinghua Cloud |
Model | FLOPs (G) | Params (M) | box AP | mask AP | config | url |
---|---|---|---|---|---|---|
RN50-CLIP | 301 | 44 | 39.3 | 36.8 | config | - |
RN50-DenseCLIP | 327 | 67 | 40.2 | 37.6 | config | Tsinghua Cloud |
RN101-CLIP | 377 | 63 | 42.2 | 38.9 | config | - |
RN101-DenseCLIP | 399 | 84 | 42.6 | 39.6 | config | Tsinghua Cloud |
To train our DenseCLIP-RN50 using RetinaNet framework, run
bash dist_train.sh configs/retinanet_denseclip_r50_fpn_1x_coco.py 8
To evaluate the box AP of RN50-DenseCLIP (RetinaNet), run
bash dist_test.sh configs/retinanet_denseclip_r50_fpn_1x_coco.py /path/to/checkpoint 8 --eval bbox
To evaluate both the box AP and the mask AP of RN50-DenseCLIP (Mask-RCNN), run
bash dist_test.sh configs/mask_rcnn_denseclip_r50_fpn_1x_coco.py /path/to/checkpoint 8 --eval bbox segm
MIT License
If you find our work useful in your research, please consider citing:
@inproceedings{rao2021denseclip,
title={DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting},
author={Rao, Yongming and Zhao, Wenliang and Chen, Guangyi and Tang, Yansong and Zhu, Zheng and Huang, Guan and Zhou, Jie and Lu, Jiwen},
booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}