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https://github.com/khanrc/tcl
Official implementation of TCL (CVPR 2023)
https://github.com/khanrc/tcl
cvpr open-world segmentation semantic-segmentation zero-shot
Last synced: 3 days ago
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Official implementation of TCL (CVPR 2023)
- Host: GitHub
- URL: https://github.com/khanrc/tcl
- Owner: khanrc
- License: mit
- Created: 2022-11-30T04:08:26.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2023-05-11T08:54:42.000Z (over 1 year ago)
- Last Synced: 2024-11-10T14:09:42.380Z (3 days ago)
- Topics: cvpr, open-world, segmentation, semantic-segmentation, zero-shot
- Language: Python
- Homepage:
- Size: 3.65 MB
- Stars: 108
- Watchers: 11
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# TCL: Text-grounded Contrastive Learning (CVPR'23)
Official PyTorch implementation of [**Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs**](https://arxiv.org/abs/2212.00785), *Junbum Cha, Jonghwan Mun, Byungseok Roh*, CVPR 2023.
**T**ext-grounded **C**ontrastive **L**earning (TCL) is an open-world semantic segmentation framework using only image-text pairs. TCL enables a model to learn region-text alignment without train-test discrepancy.
[**Demo page**](https://huggingface.co/spaces/khanrc/tcl) is available. Since this demo runs on a free HuggingFace CPU space, inference times may take around 5-10 seconds.
## Results
TCL can perform segmentation on both (a, c) existing segmentation benchmarks and (b) arbitrary concepts, such as proper nouns and free-form text, in the wild images.
Additional examples in PASCAL VOC
Additional examples in the wild
## Dependencies
We used pytorch 1.12.1 and torchvision 0.13.1.
```bash
pip install -U openmim
mim install mmcv-full==1.6.2 mmsegmentation==0.27.0
pip install -r requirements.txt
```
Note that the order of requirements roughly represents the importance of the version.
We recommend using the same version for at least `webdataset`, `mmsegmentation`, and `timm`.
## Datasets
Note that much of this section is adapted from the [data preparation section of GroupViT README](https://github.com/NVlabs/GroupViT#data-preparation).
We use [webdataset](https://webdataset.github.io/webdataset/) as scalable data format in training and [mmsegmentation](https://github.com/open-mmlab/mmsegmentation) for semantic segmentation evaluation.
The overall file structure is as follows:
```shell
TCL
├── data
│ ├── gcc3m
│ │ ├── gcc-train-000000.tar
│ │ ├── ...
│ ├── gcc12m
│ │ ├── cc-000000.tar
│ │ ├── ...
│ ├── cityscapes
│ │ ├── leftImg8bit
│ │ │ ├── train
│ │ │ ├── val
│ │ ├── gtFine
│ │ │ ├── train
│ │ │ ├── val
│ ├── VOCdevkit
│ │ ├── VOC2012
│ │ │ ├── JPEGImages
│ │ │ ├── SegmentationClass
│ │ │ ├── ImageSets
│ │ │ │ ├── Segmentation
│ │ ├── VOC2010
│ │ │ ├── JPEGImages
│ │ │ ├── SegmentationClassContext
│ │ │ ├── ImageSets
│ │ │ │ ├── SegmentationContext
│ │ │ │ │ ├── train.txt
│ │ │ │ │ ├── val.txt
│ │ │ ├── trainval_merged.json
│ │ ├── VOCaug
│ │ │ ├── dataset
│ │ │ │ ├── cls
│ ├── ade
│ │ ├── ADEChallengeData2016
│ │ │ ├── annotations
│ │ │ │ ├── training
│ │ │ │ ├── validation
│ │ │ ├── images
│ │ │ │ ├── training
│ │ │ │ ├── validation
│ ├── coco_stuff164k
│ │ ├── images
│ │ │ ├── train2017
│ │ │ ├── val2017
│ │ ├── annotations
│ │ │ ├── train2017
│ │ │ ├── val2017
```
The instructions for preparing each dataset are as follows.
### Training datasets
In training, we use Conceptual Caption 3m and 12m. We use [img2dataset](https://github.com/rom1504/img2dataset) tool to download and preprocess the datasets.
#### GCC3M
Please download the training split annotation file from [Conceptual Caption 3M](https://ai.google.com/research/ConceptualCaptions/download) and name it as `gcc3m.tsv`.
Then run `img2dataset` to download the image text pairs and save them in the webdataset format.
```
sed -i '1s/^/caption\turl\n/' gcc3m.tsv
img2dataset --url_list gcc3m.tsv --input_format "tsv" \
--url_col "url" --caption_col "caption" --output_format webdataset \
--output_folder data/gcc3m \
--processes_count 16 --thread_count 64 \
--image_size 512 --resize_mode keep_ratio --resize_only_if_bigger True \
--enable_wandb True --save_metadata False --oom_shard_count 6
rename -d 's/^/gcc-train-/' data/gcc3m/*
```
Please refer to [img2dataset CC3M tutorial](https://github.com/rom1504/img2dataset/blob/main/dataset_examples/cc3m.md) for more details.
#### GCC12M
Please download the annotation file from [Conceptual Caption 12M](https://github.com/google-research-datasets/conceptual-12m) and name it as `gcc12m.tsv`.
Then run `img2dataset` to download the image text pairs and save them in the webdataset format.
```
sed -i '1s/^/caption\turl\n/' gcc12m.tsv
img2dataset --url_list gcc12m.tsv --input_format "tsv" \
--url_col "url" --caption_col "caption" --output_format webdataset \
--output_folder data/gcc12m \
--processes_count 16 --thread_count 64 \
--image_size 512 --resize_mode keep_ratio --resize_only_if_bigger True \
--enable_wandb True --save_metadata False --oom_shard_count 6
rename -d 's/^/cc-/' data/gcc12m/*
```
Please refer to [img2dataset CC12M tutorial](https://github.com/rom1504/img2dataset/blob/main/dataset_examples/cc12m.md) for more details.
### Evaluation datasets
In the paper, we use 8 benchmarks; (i) w/ background: PASCAL VOC20, PASCAL Context59, and COCO-Object, and (ii) w/o background: PASCAL VOC, PASCAL Context, COCO-Stuff, Cityscapes, and ADE20k.
Since some benchmarks share the data sources (e.g., VOC20 and VOC), we need to prepare 5 datasets: PASCAL VOC, PASCAL Context, COCO-Stuff164k, Cityscapes, and ADE20k.
Please download and setup [PASCAL VOC](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-voc), [PASCAL Context](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#pascal-context), [COCO-Stuff164k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#coco-stuff-164k), [Cityscapes](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#cityscapes), and [ADE20k](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md#ade20k) datasets following [MMSegmentation data preparation document](https://github.com/open-mmlab/mmsegmentation/blob/master/docs/en/dataset_prepare.md).
#### COCO Object
COCO-Object dataset uses only object classes from COCO-Stuff164k dataset by collecting instance semgentation annotations.
Run the following command to convert instance segmentation annotations to semantic segmentation annotations:
```shell
python convert_dataset/convert_coco.py data/coco_stuff164k/ -o data/coco_stuff164k/
```
## Training
We use 16 and 8 NVIDIA V100 GPUs for the main and ablation experiments, respectively.
### Single node
```
torchrun --rdzv_endpoint=localhost:5 --nproc_per_node=auto main.py --cfg ./configs/tcl.yml
```
### Multi node
```
torchrun --rdzv_endpoint=$HOST:$PORT --nproc_per_node=auto --nnodes=$NNODES --node_rank=$RANK main.py --cfg ./configs/tcl.yml
```
## Evaluation
We provide [an official checkpoint](https://github.com/kakaobrain/tcl/releases/download/v1.0.0/tcl.pth) to reproduce the main results of our paper.
- Zero-shot transfer to semantic segmentation (Table 2):
```
torchrun --rdzv_endpoint=localhost:5 --nproc_per_node=auto main.py --resume checkpoints/tcl.pth --eval
```
- Evaluation without PAMR (Table 3 in Appendix):
```
torchrun --rdzv_endpoint=localhost:5 --nproc_per_node=auto main.py --resume checkpoints/tcl.pth --eval \
--opts evaluate.pamr=false evaluate.bg_thresh=0.5
```
Note that we use `bg_threshold` of 0.4 with PAMR and 0.5 without PAMR, since we observed that PAMR tends to reduce the foreground area.
## Citation
```bibtex
@inproceedings{cha2022tcl,
title={Learning to Generate Text-grounded Mask for Open-world Semantic Segmentation from Only Image-Text Pairs},
author={Cha, Junbum and Mun, Jonghwan and Roh, Byungseok},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2023}
}
```
## License
This project is released under [MIT license](./LICENSE).