Garbage_Classification

Description

[1]https://www.bilibili.com/video/BV1gz41187Hk [2]https://www.bilibili.com/video/BV1MT4y1V74w

We also provide PyTorch code for deployment on mobile device.

Tree

.
├── bin
│   ├── app_test.py
│   ├── demo.py
│   ├── my_test.py
│   └── my_train.py
├── data
├── gcnet
│   ├── classifier.py
│   ├── __init__.py
│   ├── json_utils.py
│   ├── logger.py
│   ├── __pycache__
│   ├── resnet.py
│   ├── test.py
│   ├── train.py
│   ├── transforms.py
│   └── utils.py
├── models
└── preprocess
    ├── 01.html
    ├── 01-原始数据集分布可视化分析.py
    ├── 02-原始数据集train-val划分.py
    ├── 03.html
    ├── 03-train和val数据分布可视化.py
    ├── 04.html
    ├── 04-四大类垃圾分布可视化.py
    ├── 05.html
    ├── 05-四大类垃圾train-val分布可视化.py
    ├── 06-数据增强transform.py
    ├── 07-原始数据可视化.py
    ├── 08-预处理数据加载.py
    ├── 09-测试resnext101模型.py
    ├── 10-Web服务环境搭建.py
    ├── 11-分类网络环境搭建.py
    └── images

Demo

python ./demo.py

Train

You should download data and models from BaiduYun: https://pan.baidu.com/s/1g9RoIGxf2OD1zo4bgbMQWg password: cdz5

python ./my_train.py

Test

python ./my_test.py

Web

python ./app_test.py

Experiment

Model	Iter	precision	recall	f1-score
resnext101_32x16d	10	0.9827	0.9826	0.9826
resnext101_32x8d	30	0.9589	0.9588	0.9583
resnext101_32x8d	10	0.9473	0.9472	0.9472
resnet18	10	0.8968	0.8959	0.8940

• resnext101_32x16d

LR	epoch	Train Loss	Valid Loss	Train Acc.	Valid Acc.
0.001000	1.000000	0.281912	0.241702	90.296428	91.276075
0.001000	2.000000	0.177333	0.147571	93.530952	94.628832
0.001000	3.000000	0.163498	0.135344	94.181235	95.118656
0.001000	4.000000	0.148997	0.081726	94.586606	96.968161
0.001000	5.000000	0.133063	0.090255	95.110210	96.807702
0.001000	6.000000	0.125995	0.069795	95.346677	97.415759
0.001000	7.000000	0.122259	0.102625	95.642260	96.351659
0.001000	8.000000	0.127478	0.068116	95.422684	97.660671
0.001000	9.000000	0.132976	0.053337	95.312896	98.268727
0.001000	10.000000	0.118554	0.068123	95.616924	97.669116