NanoDet-rat

use nanodet to detect rat.

Introduction

NanoDet is a FCOS-style one-stage anchor-free object detection model which using Generalized Focal Loss as classification and regression loss.

In NanoDet-Plus, we propose a novel label assignment strategy with a simple assign guidance module (AGM) and a dynamic soft label assigner (DSLA) to solve the optimal label assignment problem in lightweight model training. We also introduce a light feature pyramid called Ghost-PAN to enhance multi-layer feature fusion. These improvements boost previous NanoDet's detection accuracy by 7 mAP on COCO dataset.

NanoDet-Plus 知乎中文介绍

NanoDet 知乎中文介绍

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Install

Requirements

Linux or MacOS
CUDA >= 10.0
Python >= 3.6
Pytorch >= 1.7
experimental support Windows (Notice: Windows not support distributed training before pytorch1.7)

Step

1.下载代码

2.安装依赖包

pip install -r requirements.txt

3.环境编译

python setup.py develop

How to Train

Prepare dataset

If your dataset annotations are pascal voc xml format, refer to config/nanodet_custom_xml_dataset.yml

Or convert your dataset annotations to MS COCO format(COCO annotation format details).
Prepare config file

Copy and modify an example yml config file in config/ folder.

Change save_path to where you want to save model.

Change num_classes in model->arch->head.

Change image path and annotation path in both data->train and data->val.

Set gpu ids, num workers and batch size in device to fit your device.

Set total_epochs, lr and lr_schedule according to your dataset and batchsize.

If you want to modify network, data augmentation or other things, please refer to Config File Detail
Start training

NanoDet is now using pytorch lightning for training.

For both single-GPU or multiple-GPUs, run:
```
python tools/train.py CONFIG_FILE_PATH
```
Visualize Logs

TensorBoard logs are saved in save_dir which you set in config file.

To visualize tensorboard logs, run:
```
cd <YOUR_SAVE_DIR>
tensorboard --logdir ./
```

2anchao / naodet-rat