The topic was from huawei cloud garbage classification competition. To learn how to use pytorch and test the effect of the backbone(Resnet, ResNext, Se_ResNext, etc).

The project is only for learning, and i'm have not paticipate in that competition.

In order to better experience the learning process, I also extended the dataset by downloading pictures of corresponding categories at Google and Baidu.

I used some models to overfit the original training data, and then used those models to distinguish the new additions. and the first version of mydataset And then there are a lot of problems with the data set that haven't been solved very well, so I'll offer both the original data set and my data set.

• raw_dataset(garbage_classify_v2.zip) in official website
• mydataset_v1 in BaiduYun

The model was finally used Se_ResNext101_64x4d pretrained in imagenet.

For better performance, i alos use CBAM and FocalLoss Module to help training.

• feature extractor
• organize the code
• test model

• pytorch 1.3.1 or above
• numpy
• PIL
• pretrainedmodels

Since the equipment I can use is limited, the test data I conducted are for reference only!

se_resnext101_32x4d + fc

se_resnext101_32x4d + cbam + fc