Mosquito Classification

This is the 7th place solution for the MosquitoAlert Challenge 2023. The goal of this competition is to identify mosquitoes and determine their species.

How to Run CLIP Classifier

1. Install Datasets

   • Download the competition dataset from here and unzip it to a folder named data_round_2 (the annotations files are included).
   • Install lux's dataset, unzip gbif-cropped and inaturalist-six-cropped (the annotations files are included).

2. Install Dependencies

   • Use the following command to install the necessary dependencies: pip install -r requirements.txt.

3. Run the Classifier

   • Navigate to the experiments directory and execute the following command: python mosquito_clf_yolo_lux_ema.py.

How to Train YOLOv8-s Model

1. Install Competition Dataset

   • Download the competition dataset from here and unzip it to a folder named data_round_2.

2. Install Dependencies

   • Use the following command to install the necessary dependencies: pip install -r requirements.txt.

3. Prepare YOLO Dataset

   • Navigate to the experiments/yolo directory and run the script: python convert_mosquito_to_yolo.py.

4. Start Training

   • Execute the command: python yolo_training.py.

Model Weights

You can find the model weights and instructions on how to use them on the Hugging Face Model Hub.

Annotation Files

data_round_2

• phase2_train_v0_cleaned.csv was created using owl-vit. You can refer to experiments/cleaning_annotations.ipynb for details.
• phase2_train_v0_cleaned_yolo_best_annotations.csv uses phase2_train_v0_cleaned.csv along with YOLOv8-s model annotations. Refer to extra_data/annotate_images_yolo.py for more information.
• best_model_val_data_yolo_annotations.csv and best_model_train_data_yolo_annotations.csv are train/validation splits of phase2_train_v0_cleaned_yolo_best_annotations.csv.

gbif-cropped and inaturalist-six-cropped

• inaturalist.csv contains annotations for lux's dataset. Since the images are already cropped, we used the entire image as the bounding box.