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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

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