baidu salers logo prediction
Basic info
- This project is based on gluon or a more famous parent: mxnet.
Project code structure
- dataloader: data preprocess
- dataPreProcess.py
- 80% of dataset used for train, 20% of dataset used for valid.
- augmentation.py
- add padding, rotate
- ColorJitterAug: 0.3
- random gray, prob: 0.5
- calc dataset mean and std（utils/calc_mean_std.py），used for dataset normalize
- dataPreProcess.py
- logs
- training logs
- model
- define model
- utils
- calc_mean_std.py
- calc dataset mean and std，used for dataset normalize
- calc_mean_std.py
- weights
- save model weights
- train.py
- train model code
- change pretrianed_model_name for different pretrained models, pretrained models
- predict.py
- predict code for test and valid
- predict_wrong_ana.py
- predict valid dataset label, find what's wrong with model
- used for find best concat params
- concateResult.py
- embedding model predict result
Future Work
- valid data can join train, use cv for valid
- mixup：BEYOND EMPIRICAL RISK MINIMIZATION
- pseudo label
- more augmentation
- more init methods