This repository contains the code and resources for the article " Weakly Supervised Detection of Marine Animals in High Resolution Aerial Images ".
Developed using Python version 3.7.1.
pytorch>=1.8
torchvision>=0.8.1
keepsake>=0.4.2
opencv>=4.1.2
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
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 |
If this work is useful to your research, consider citing the following article:
@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}
}
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent