# C_DenseNet_for_wheat_stripp_rust **Repository Path**: djcc/c_-dense-net_for_wheat_stripp_rust ## Basic Information - **Project Name**: C_DenseNet_for_wheat_stripp_rust - **Description**: No description available - **Primary Language**: Unknown - **License**: Not specified - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2021-11-04 - **Last Updated**: 2021-11-04 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README # C-DenseNet-for-wheat-stripe-rust- ## Table of Contents 1.Environment 2.Download 3.How2train 4.Reference ## Environment ubuntu 16.04 torch == 1.2.0 ## Download We have provided the training results of C-DenseNet, you can use test.py to test the classification effect of the model: https://pan.baidu.com/s/1sqE1hwszvaozl06aKPd6wQ Extraction code:tw36 the test pictures are put in ./dataset/test/ ## How2train ### Create an environment *conda create -n pytorch python=3.6 *conda install pytorch==1.2.0 torchvision==0.4.0 cudatoolkit=10.0 *pip install -r requirements.txt ### train *Before training, you need to change all the file paths in train.py and dataset_nocsv.py to your path *The labels in dataset_nocsv.py need to be replaced with the label name of your training set *python train.py ### test *Before testing, you need to change all the file paths in test.py and dataset_nocsv.py to your path *The class_names in test.py need to be consistent with the class name order during training *python test.py ## Reference https://blog.csdn.net/hacker_long/article/details/100138454?ops_request_misc=%257B%2522request%255Fid%2522%253A%2522159495422819725211965258%2522%252C%2522scm%2522%253A%252220140713.130102334.pc%255Fblog.%2522%257D&request_id=159495422819725211965258&biz_id=0&utm_medium=distribute.pc_search_result.none-task-blog-2~blog~first_rank_v1~rank_blog_v1-2-100138454.pc_v1_rank_blog_v1&utm_term=pytorch+%E5%9B%BE%E5%83%8F%E5%88%86%E7%B1%BB