https://github.com/CVIR/SLM
This repository contains the official implementation of SLM (WACV 2023) https://arxiv.org/pdf/2012.03358.pdf.
https://github.com/CVIR/SLM
Last synced: about 2 months ago
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This repository contains the official implementation of SLM (WACV 2023) https://arxiv.org/pdf/2012.03358.pdf.
- Host: GitHub
- URL: https://github.com/CVIR/SLM
- Owner: CVIR
- License: apache-2.0
- Created: 2023-01-04T19:25:15.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-01-04T19:53:23.000Z (almost 3 years ago)
- Last Synced: 2024-12-16T02:34:27.568Z (10 months ago)
- Language: Python
- Size: 946 KB
- Stars: 6
- Watchers: 5
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- Awesome-Mixup - [Code
README
# Select, Label, and Mix (SLM)
The repository contains the codes for the paper "Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation" part of Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2023.
[Aadarsh Sahoo1](https://aadsah.github.io/), [Rameswar Panda1](https://rpand002.github.io/), [Rogerio Feris1](https://www.rogerioferis.org/), [Kate Saenko1,2](http://ai.bu.edu/ksaenko.html), [Abir Das3](https://cse.iitkgp.ac.in/~adas/)
1 MIT-IBM Watson AI Lab, 2 Boston University, 3 IIT Kharagpur
[[Paper]](https://openaccess.thecvf.com/content/WACV2023/papers/Sahoo_Select_Label_and_Mix_Learning_Discriminative_Invariant_Feature_Representations_for_WACV_2023_paper.pdf) [[Project Page]](https://cvir.github.io/projects/slm)
### Preparing the Environment
#### Conda
Please use the `slm_environment.yml` file to create the conda environment `SLM` as:
```
conda env create -f slm_environment.yml
```
#### Pip
Please use the `requirements.txt` file to install all the required dependencies as:
```
pip install -r requirements.txt
```
### Data Directory Structure
All the datasets should be stored in the folder `./data` following the convention `./data//`. E.g. for `Office31` the structure would be as follows:
```
.
├── ...
├── data
│ ├── Office31
│ │ ├── amazon
│ │ ├── webcam
│ │ ├── dslr
│ └── ...
└── ...
```
For using datasets stored in some other directories, please update the path to the data accordingly in the txt files inside the folder `./data_labels`.
The official download links for the datasets used for this paper are:
**Office31**: https://people.eecs.berkeley.edu/~jhoffman/domainadapt/#datasets_code
**OfficeHome**: http://hemanthdv.org/OfficeHome-Dataset/
**ImageNet-Caltech**: http://www.image-net.org/, http://www.vision.caltech.edu/Image_Datasets/Caltech256/
**VisDA-2017**: http://ai.bu.edu/visda-2017/#download
### Training SLM
Here is a sample and recomended command to train SLM for the transfer task of `Amazon -> Webcam` from `Office31` dataset:
```
CUDA_VISIBLE_DEVICES=0 python main.py --manual_seed 1 --dataset_name Office31 --src_dataset amazon --tgt_dataset webcam --batch_size 64 --model_root ./checkpoints_a31_w10 --save_in_steps 500 --log_in_steps 10 --eval_in_steps 10 --model_name resnet50 --classifier_name resnet50 --source_images_path ./data_labels/Office31/amazon_31_list.txt --target_images_path ./data_labels/Office31/webcam_10_list.txt --pseudo_threshold 0.3 --warmstart_models True --num_iter_adapt 10000 --num_iter_warmstart 5000 --learning_rate 0.0005 --learning_rate_ws 0.001
```
For detailed description regarding the arguments, use:
```
python main.py --help
```
### Citing SLM
If you use codes in this repository, consider citing SLM. Thanks!
```
@inproceedings{sahoo2023select,
title={Select, label, and mix: Learning discriminative invariant feature representations for partial domain adaptation},
author={Sahoo, Aadarsh and Panda, Rameswar and Feris, Rogerio and Saenko, Kate and Das, Abir},
booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},
pages={4210--4219},
year={2023}
}
```