DOLG - GitLite

KO | EN

gitlite — search

#ai #typescript #golang #go #docker #nodejs #java #windows #awesome #kubernetes #vue #security #mcp #open-source

DOLG

(★ 66)

Pytorch Implementation of DOLG (ICCV 2021)

File Explorer

Download Latest Version (.zip)

- resnet101_delg_8gpu.yaml
- - dolg_model.py
  - resnet.py
- __init__.py
- benchmark.py
- builders.py
- checkpoint.py
- config.py
- distributed.py
- io.py
- logging.py
- meters.py
- net.py
- optimizer.py
- timer.py
- trainer.py
- - landmark
- __init__.py
- commondataset.py
- loader.py
- transforms.py
- infer.py
- process.py
- util.py
- DATA.md
- figure2.pdf
- figure2.png
- orthogo.pdf
- result1.png
- result2.png
- result3.png
- result4.png
- result_overview.png
- - roxford_R101_512_crop_test1.pickle
  - roxford_R101_512_crop_test3.pickle
  - roxford_R101_512_crop_test5.pickle
  - roxford_R101_512_test5.pickle
  - roxford_R50_512_crop_test5.pickle
  - roxford_R50_512_test5.pickle
  - rparis_R101_512_crop_test5.pickle
  - rparis_R101_512_test5.pickle
  - rparis_R50_512_crop_test5.pickle
  - rparis_R50_512_test5.pickle
- - gnd_roxford5k.pkl
  - gnd_rparis6k.pkl
- - download.sh
- compute.py
- dataset.py
- example_evaluate.py
- README.md
- run_extractor.sh
- run_train.sh
init_path.py
LICENSE
README.md
requirements.txt
train.py

# Installation Guide

1. Get the code

git clone https://github.com/feymanpriv/DOLG

Downloads the entire project code from GitHub to your computer.

cd DOLG

Moves into the project folder you just downloaded.

2. Python

Easy Recommended

Prerequisites

Git Needed to download the project code from GitHub.
Python 3 On Windows, be sure to check 'Add Python to PATH' during installation.

pip install -r requirements.txt

Installs the Python libraries listed in requirements.txt (or similar).

✅ If it runs without errors and prints output in the terminal, it worked.

Pulled directly from this repo's README.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# DOLG: Single-Stage Image Retrieval with Deep Orthogonal Fusion of Local and Global Features (ICCV 2021)

## Pipeline
<p align="center"><img width="90%" src="imgs/figure2.png" /></p>

## Performances
<p align="center"><img width="100%" src="imgs/result_overview.png" /></p>

**modified results (should follow cropping results)**

|  			 					 | Roxf-M | +1M | Rpar-M | +1M   | Roxf-H | +1M  | Rpar-H | +1M  |
|:------------------------------:|:------:|:---:|:------:|:-----:|:------:|:----:|:------:|:----:|
|  DOLG-R50(with query cropping) |  81.20 |71.36| 90.07  | 78.99 |  62.55 |47.34 | 79.20  | 59.75|
|  DOLG-R101(with query cropping)|  82.37 |73.63| 90.97  | 80.44 |  64.93 |51.57 | 81.71  | 62.95|
|                                                                                                |
|  DOLG-R50(w/o query cropping)  |  82.38 |77.78| 90.94  | 82.16 |  62.92 | 55.48| 80.48  | 65.77| 
|  DOLG-R101(w/o query cropping) |  83.22 |78.96| 91.64  | 82.89 |  64.83 | 57.86| 82.56  | 67.34|

+1M results is updated.

## Codes

### Requirements

- NVIDIA GPU, Linux, Python3(tested on 3.6.10)
- Tested with CUDA 10.2, cuDNN 7.1 and PyTorch 1.4.0

```
pip install -r requirements.txt
```

### Training

1. Find datasets via symlinks from `datasets/data` to the actual locations where the dataset images and annotations are stored. Refer to [`DATA.md`](imgs/DATA.md).

2. Set datapath, model, training parameters in configs/resnet101_delg_8gpu.yaml and run

```
python train.py \
    --cfg configs/resnet101_delg_8gpu.yaml \
    OUT_DIR ./output \
    PORT 13001 \
    TRAIN.WEIGHTS ./pretrained/R-101-1x64d_dds_8gpu.pyth
```

### Evaluation

1. ROxf and RPar feature extraction, set ${total_num}=1 and run

```
python evaler/infer.py --cfg configs/resnet101_delg_8gpu.yaml
```

2. 1M distractor feature extraction, set ${total_num} = n * ${gpu_cards} in **configs/resnet101_delg_8gpu.yaml** and run

```
sh scripts/run_extractor.sh configs/resnet101_delg_8gpu.yaml
```

3. Eval on ROxf and RPar, refer [`README.md`](revisitop/README.md) for data fetch and description. Groudtruth file and some examples are prepared in [revisitop](https://github.com/feymanpriv/DOLG/tree/main/revisitop).

### Wights

- [R-50-DOLG](https://pan.baidu.com/s/13Bt1v_sMSny8pGRIM0K8xQ) (d7or)   - [R-101-DOLG](https://pan.baidu.com/s/1_osEyC53txA-_1cbadBzLQ) (nqdb)

## Citation

If the project helps your research, please consider citing our paper as follows.

```BibTeX
@InProceedings{Yang_2021_ICCV,
    author={Yang, Min and He, Dongliang and Fan, Miao and Shi, Baorong and Xue, Xuetong and Li, Fu and Ding, Errui and Huang, Jizhou},
    title={DOLG: Single-Stage Image Retrieval With Deep Orthogonal Fusion of Local and Global Features},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month={October},
    year={2021},
    pages={11772-11781}
}

```

## References

pycls(https://github.com/facebookresearch/pycls)
pymetric(https://github.com/feymanpriv/pymetric)
DELG(https://github.com/feymanpriv/DELG)
Parsing-R-CNN(https://github.com/soeaver/Parsing-R-CNN)