Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"

<strong>DESCRIPTION</strong> 📓 "data" folder directory contains the datasets for classification and detection. The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images. The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images The "weights" folder contains pretrained models for classification and detection tasks. The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia. The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>. You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below. <em>Classification weights</em> EfficientNetv2-M <strong>ResNeSt-101e</strong> (🚀 RECOMMENDED) ResNet-101d ReXnet-100 SeResNet-152d <em>Detection weights</em> YOLOR-W6-1280 YOLOX-X-640 YOLOv5-X-640 YOLOv5-X-1280 YOLOv5-M6-1280 <strong>YOLOv5-L6-1280</strong> (🚀 RECOMMENDED) Read README.md file for more details