CWLD_model project shows scripts and instructions on how to use this dataset to train a segmentation model. requirements.txt files provide the libraries you need to run your project. The README.md document details the deployment process and features of each module. You can also visit the GitHub page for scripts and instructions on how to use this dataset for visualizing and plotting basic statistics. The models and the code to execute them are released on https://github.com/huangleinxidimejd/CWLD_Model. Training details The model was trained with two GPUs, an Nvidia GeForce RTX 2080Ti, and the following parameters: 'train_batch_size': 4, 'val_batch_size': 4, 'train_crop_size': 512, 'val_crop_size': 512, 'lr': 0.001, # the learning rate used during training. It determines how quickly the model learns from the data 'Epoch Times': 200, 'gpu': correct, 'weight_decay': 5E-4, 'Momentum': 0.9, 'print_freq': 100, 'predict_step': 5, usage After downloading the dataset from Zenodo, place the train and val files from the Deep Learning Datasets file into the data folder of the CWLD semantic segmentation model. Open: CWLD_ Open the root directory in CWLD_model/dataset/ and start training with the WasteSeg_Train.py file. The modelss module provides five convolutional networks, Improved_DeeplabV3_plus, PSPNet, ResNet, SegNet, and UNet, which can be selected and modified accordingly. The utils package provides a large number of data processing tools to use. The trained model can be predicted from a EvalSeg.py file.