PyTorch Project for global-wheat-detection

This project is based on https://github.com/L1aoXingyu/Deep-Learning-Project-Template

And I remove the high-leval api ignite which makes the project not easy to change training loop.

Requirements

• yacs (Yet Another Configuration System)
• PyTorch (An open source deep learning platform)

Table Of Contents

• In Details
• Future Work
• Contributing
• Acknowledgments

In Details

├──  config
│    └── defaults.py  - here's the default config file.
│
│
├──  configs  
│    └── train_mnist_softmax.yml  - here's the specific config file for specific model or dataset.
│ 
│
├──  data  
│    └── datasets  - here's the datasets folder that is responsible for all data handling.
│    └── transforms  - here's the data preprocess folder that is responsible for all data augmentation.
│    └── build.py  		   - here's the file to make dataloader.
│    └── collate_batch.py   - here's the file that is responsible for merges a list of samples to form a mini-batch.
│
│
├──  engine
│   └── average.py   -this file contains the average method.
│   └── fitter.py     - this file contains the train loops.
|   └── inference.py   - this file contains the inference process.
│
│
├── layers              - this folder contains any customed layers of my project.
│   └── fpn_backbone.py  -this file contains the backbone function with fpn
│   └── label_smooth_crossentropy.py     -this file contains the label smooth crossentropy loss
│
├── modeling            - this folder contains any model of my project.
│   └── wheat_detector.py   -this file defines the wheat_detector model based on fasterrcnn
│
│
├── solver             - this folder contains optimizer of my project.
│   └── build.py            -this file contains optimizer function
│   └── lr_scheduler.py       -this file contains lr_scheduler function
│   
│ 
├──  tools                - here's the train/test model of my project.
│    └── train_net.py  - here's the pipeline of train model that is responsible for the whole pipeline.
|    └── test_net.py  - here's the pipeline  of train model that is responsible for the whole pipeline.
│ 
│ 
└── utils
│    ├── logger.py
│    └── any_other_utils_i_need
│ 
│ 
└── tests					- this foler contains unit test of my project.
     ├── test_data_sampler.py

Future Work

Contributing

Any kind of enhancement or contribution is welcomed.

Acknowledgments