Top 3% (7/336) solution for iWildCam 2019 competition (Categorize animals in the wild), which is as part of the FGVC6 workshop at CVPR 2019

Thanks to my team members!

Please view the detailed report Efficient Method for Categorize Animals in the Wild.

• Python 3.6
• pytorch 1.1.0

Download the competition data according to here

After downloading, save the image-file name as CSV format.

python prep_data.py

python detect_crop_image.py

In my method, I first run object detection and crop the bounding box, then use the cropped image for classification.

python train_model.py

python infer.py

I got the best single model prediction result (f1=0.224 in private LB) with the following configuration:

model: efficientnet_b0 (imagenet pretrained)

image augmentation: traditional image augmentation + CLAHE + gray scale + cutout + mixup + label smoothing