Fossil Image Identification (Classification)

Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews

Identification of fossil species is crucial to evolutionary studies. Recent advances from deep learning have shown promising prospects in fossil image identification. However, the quantity and quality of labeled fossil images are often limited due to fossil preservation, conditioned sampling, and expensive and inconsistent label annotation by domain experts, which pose great challenges to training deep learning based image classification models. To address these challenges, we follow the idea of the wisdom of crowds and propose a multiview ensemble framework, which collects Original (O), Gray (G), and Skeleton (S) views of each fossil image reflecting its different characteristics to train multiple base models, and then makes the final decision via soft voting.

Reference

If you find this work is useful, please consider the following citation.

@article{hou2023fossil,
  title={Fossil Image Identification using Deep Learning Ensembles of Data Augmented Multiviews},
  author={Hou, Chengbin and Lin, Xinyu and Huang, Hanhui and Xu, Sheng and Fan, Junxuan and Shi, Yukun and Lv, Hairong},
  journal={Methods in Ecology and Evolution},
  year={2023}
}

paper link: https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.14229

Install

conda create -n MulEnsID python=3.10.8    
conda activate MulEnsID
conda install pytorch==1.13.0 torchvision==0.14.0 torchaudio==0.13.0 pytorch-cuda=11.6 -c pytorch -c nvidia
conda install pyyaml=6.0
conda install huggingface_hub=0.10.1
pip install opencv-python==4.6.0.66
pip install -U scikit-learn==1.1.1
conda install scikit-image=0.19.3

Usage

ResNet-50

CUDA_VISIBLE_DEVICES=0 python train.py data/Original_img --lr 0.01 -b 32 --epochs 500 --output ./output/resnet50 --model resnet50 --num-classes 16 --pretrained  --experiment ori-view 
CUDA_VISIBLE_DEVICES=0 python train.py data/gray --lr 0.1 -b 64 --epochs 500 --output ./output/resnet50 --model resnet50 --num-classes 16 --pretrained  --experiment gray-view 
CUDA_VISIBLE_DEVICES=0 python train.py data/skeleton --lr 0.1 -b 32 --epochs 500 --output ./output/resnet50 --model resnet50 --num-classes 16 --pretrained  --experiment skeleton-view 
CUDA_VISIBLE_DEVICES=0 python voting.py --view1 data/Original_img/test/ --view2 data/gray/test/ --view3 data/skeleton/test/ -cp1 ./output/resnet50/ori-view/model_best.pth.tar -cp2 ./output/resnet50/gray-view/model_best.pth.tar -cp3 ./output/resnet50/skeleton-view/model_best.pth.tar --model resnet50  --num-classes 16

EfficientNet-b2

CUDA_VISIBLE_DEVICES=0 python train.py data/Original_img --lr 0.01 -b 32 --epochs 500 --output ./output/efficientnet-b2 --model efficientnet_b2 --num-classes 16 --pretrained  --experiment ori-view
CUDA_VISIBLE_DEVICES=0 python train.py data/gray --lr 0.1 -b 128 --epochs 500 --output ./output/efficientnet-b2 --model efficientnet_b2 --num-classes 16 --pretrained  --experiment gray-view 
CUDA_VISIBLE_DEVICES=0 python train.py data/skeleton --lr 0.1 -b 32 --epochs 500 --output ./output/efficientnet-b2 --model efficientnet_b2 --num-classes 16 --pretrained  --experiment skeleton-view
CUDA_VISIBLE_DEVICES=0 python voting.py --view1 data/Original_img/test/ --view2 data/gray/test/ --view3 data/skeleton/test/ -cp1 ./output/efficientnet-b2/ori-view/model_best.pth.tar -cp2 ./output/efficientnet-b2/gray-view/model_best.pth.tar -cp3 ./output/efficientnet-b2/skeleton-view/model_best.pth.tar --model efficientnet_b2  --num-classes 16

Dataset

Please see the README.md under the 2400_fus folder.

Data Preprocessing

python datasplit.py --input ./2400_fus --output ./data/Original_img --train-rate 0.734 --val-rate 0.5  # seed=2022 with Microsoft Windows 11 version 21H2 for the data used in our paper
python multiview.py --input ./data/Original_img --output ./data --gray --binary --blocksize 41 --C 5 --skeletonize