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https://github.com/pangoraw/marinemammalsdetection
:dolphin: Code accompanying the article "Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images"
https://github.com/pangoraw/marinemammalsdetection
animal-detection marine-data remote-sensing
Last synced: 26 days ago
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:dolphin: Code accompanying the article "Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images"
- Host: GitHub
- URL: https://github.com/pangoraw/marinemammalsdetection
- Owner: Pangoraw
- Created: 2021-08-12T08:01:30.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2022-02-23T09:49:00.000Z (over 2 years ago)
- Last Synced: 2024-01-29T21:12:42.289Z (9 months ago)
- Topics: animal-detection, marine-data, remote-sensing
- Homepage: https://www.mdpi.com/2072-4292/14/2/339
- Size: 7.81 KB
- Stars: 12
- Watchers: 3
- Forks: 2
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
## Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images
This repository contains the code and resources for the article " Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images ".
### Dependencies
Developed using Python version `3.7.1`.
```
pytorch>=1.8
torchvision>=0.8.1
keepsake>=0.4.2
opencv>=4.1.2
```
### Architecture
Clone using the following command:
```
git clone --recurse-submodules https://github.com/Pangoraw/MarineMammalsDetection
```
The folder structure is the following:
```
└──code
├── examples # scripts consuming the padim library for training + testing
│ ├── main.py # main entry point
│ ├── mvtec.py # training mvtec dataset
│ ├── padeep.py # training deep methods for semmacape/kelonia/ifremer datasets
│ ├── semmacape.py # training regulart methods for semmacape/kelonia/ifremer
│ ├── test_mvtec.py # testing pipeline for the mvtec ad dataset
│ ├── test_semmacape.py # testing pipeline for the semmacape/kelonia/ifremer dataset
│ └── train_test.sh # bash script to launch main.py on the slurm cluster with a config
├── padim # main library code and implementations of the models
│ ├── backbones # various encoding backbones
│ ├── base.py # common encoding for all implementations
│ ├── datasets # torch utils datasets for the project
│ ├── deep_svdd # deep-svdd implementation of a MLP
│ ├── multi_svdd.py # multi-headed deep-svdd
│ ├── padim.py # regular padim
│ ├── padim_shared.py # padim with a single shared Gaussian
│ ├── padim_svdd.py # padim with a multi headed deep-svdd
│ ├── panf.py # padim with a multi headed normalizing flow
│ └── utils
│ ├── utils.py # various utilities from the great https://github.com/taikiinoue45/PaDiM
│ ├── distance.py # mahalanobis distance GPU implementation
│ └── regions.py # region proposals and utilities
└── configs # pre-defined configs to use with examples/train_test.sh
```
### Main script (`examples/main.py`)
The main entry point is `examples/main.py` which is going to train + test the specified model. If the file at location `params_path` already exists, the script is going to load it and only perform testing for reproducibility.
| Parameter | Description |
| --------- | ----------- |
|`train_folder TRAIN_FOLDER`|Training image folder location|
|`test_folder TEST_FOLDER`|Testing image folder location|
|`params_path PARAMS_PATH`|Models parameters saving path|
|`train_limit TRAIN_LIMIT`|# of training samples (optional)|
|`load_path LOAD_PATH`|Encoder weights param file (optional)|
|`threshold THRESHOLD`|Patch anomaly threshold (optional)|
|`test_limit TEST_LIMIT`|# of testing samples (optional)|
|`iou_threshold IOU_THRESHOLD`|IoU threshold for positive predicitions|
|`min_area MIN_AREA`|Minimum area for boxes, smaller ones are filtered out (optional)|
|`use_nms`|Use non-maximum suppression (optional)|
|`shared`|Train a single shared Gaussian estimator (optional)|
|`deep`|Use a "deep" method like PaDiM+Deep-SVDD or PaDiM+NF (optional)|
|`semi_ortho`|Use semi-orthogonal encoding instead of random dimensions selection (optional)|
|`compare_all`|Use cross Mahalanobis distance between all patches and all Gaussian estimators|
|`size SIZE [default=416x416]`|Input image size|
|`oe_folder OE_FOLDER`|Outlier images folder (optional)|
|`oe_frequency OE_FREQUENCY`|Outlier image frequency (2 = 50% of images are outliers, 3 = 1/3,etc...)|
|`n_epochs N_EPOCHS`|Number of training epochs for deep methods|
|`ae_n_epochs AE_N_EPOCHS`|Number of pre-training epochs for Deep-SVDD|
|`n_svdds N_SVDDS`|Number of heads for deep methods|
|`pretrain`|Use pretraining for Deep-SVDD|
|`use_self_supervision`|Use self supervised loss|
|`num_embeddings NUM_EMBEDDINGS`|Size of embedding vectors|
|`backbone {resnet18,resnet50,wide_resnet50}`|Which encoding backbone to use|
|`nf`|Whether to use Deep-SVDD or NF for deep methods|
### Citing
If this work is useful to your research, consider citing the following article:
```bib
@Article{rs14020339,
AUTHOR = {Berg, Paul and Santana Maia, Deise and Pham, Minh-Tan and Lefèvre, Sébastien},
TITLE = {Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images},
JOURNAL = {Remote Sensing},
VOLUME = {14},
YEAR = {2022},
NUMBER = {2},
ARTICLE-NUMBER = {339},
URL = {https://www.mdpi.com/2072-4292/14/2/339},
ISSN = {2072-4292},
DOI = {10.3390/rs14020339}
}
```