toannguyen1904 / DeWi

Official code of the paper "Deep-Wide Learning Assistance for Insect Pest Classification"

Deep-Wide Learning Assistance for Insect Pest Classification

Official code of "Deep-Wide Learning Assistance for Insect Pest Classification."

We propose DeWi, the novel learning assistance for insect pest classification. With a one-stage and alternating training strategy, DeWi simultaneously enhances several Convolutional Neural Networks in two perspectives: discrimination (by leveraging the benefit of triplet margin loss in a supervised training manner) and generalization (with the help of data augmentation). From that, DeWi can learn discriminative and in-depth features of insect pests (deep) yet still generalize well to a large number of categories (wide).

Our DeWi models are better at focusing on meaningful features than the baseline methods.

Getting Started

We highly recommend you to create a separate conda environment for the project. Please follow the below steps to set up the environment and install the necessary packages.

conda create -n dewi python=3.8
conda activate dewi
conda install pytorch pytorch-cuda=11.6 -c pytorch -c nvidia
conda install pip
pip install -r requirements.txt

Data

Download the IP102 dataset from this URL and the D0 dataset from this URL. After downloading, change the dataset_path in config.py to the path of the images folder.

Running script

• Change the root and checkpoint_path in config.py to the appropriate paths.

• Assume you use the DeWi model with ResNet-152 variant, then run the following command to start training:

    python3 train.py dewi_resnet152
  

  After training each epoch, the validation and testing phases are automatically executed. The log files and checkpoint models are saved in the checkpoint_path. Replace name = k[:] by name = k[7:] in utils/auto_load_resume if you want to training a model which is trained on multiple GPUs.

Contact

For any concerns, please contact Nguyen Thanh Binh (Associate Professor at University of Science Ho Chi Minh city) via [e-mail].