Object detection dataset:

https://www.kaggle.com/datasets/eashenyang/mosquito-alert-2023-detection

Image classification dataset:

https://www.kaggle.com/datasets/eashenyang/mosquito-alert-2023-classification

Object detection dataset: train/detection/dataset

dataset
├── aegypti
├── albopictus
└── yolo_f0_raw

Image classification dataset: train/classification/dataset

dataset
├── origin_dataset
└── inaturalist

cd train/detection
sh train.sh

get your best model under train/detection/runs/train directory

Download pretrain model from following link

https://drive.google.com/file/d/17sUNST7ivQhonBAfZEiTOLAgtaHa4F3e/view?usp=sharing

Place metafg_2_inat21_384.pth at train/classification

Using following commands, you will get 10 models from 10 train/val splits

cd train/classification
sh batch_train.sh

Using following commands, you will get uniform soup model from 10 models

cd train/classification
python avg_ckpt.py {your_args}

The original dataset underwent the removal of images containing multiple mosquitoes, and bounding boxes were manually refined. Additionally, an additional dataset was compiled from iNaturalist, featuring Aedes aegypti and Aedes albopictus species, with mosquito bounding boxes also being manually labeled.

In the original dataset, images with noisy labels were eliminated. An additional dataset was acquired from iNaturalist, and the mosquito class for each entry was meticulously verified through manual inspection.