# yolo_nano

**Repository Path**: hdfsun/yolo_nano

## Basic Information

- **Project Name**: yolo_nano
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-03-23
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Introduction

YOLO nano is from this [paper](https://arxiv.org/abs/1910.01271).

# TODO
Since I'm too busy at the end of the semester, I will continue working on this project after my exams.
- [x] Finish a draft version of implementation
- [x] Add README
- [x] Add checkpoint support
- [x] Add COCO dataset support (Code still needs cleaning. I'm working on it.)
- [x] Add _multi scale_ and _horizontal flip_ transforms
- [x] Reconstruct the code of visualizer
- [x] Add val and test
- [ ] Add VOC support
- [ ] Test accuracy

# Installation
```bash
git clone https://github.com/liux0614/yolo_nano
pip3 install -r requirements.txt
```

# COCO

## Project Structure
<pre>
root/
  results/
  datasets/
    coco/
      images/
        train/
        val/
      annotation/
        instances_train2017.json
        instances_val2017.json
</pre>

## Train
To use COCO dataset loader, _pycocotools_ should be installed via the following command.
```bash 
pip install "git+https://github.com/philferriere/cocoapi.git#egg=pycocotools&subdirectory=PythonAPI"
```

To train on COCO dataset:
```bash
python3 main.py --dataset_path datasets/coco/images --annotation_path datasets/coco/annotation/instances_train2017.json 
                --dataset coco --lr 0.0001 --conf_thres 0.8 --nms_thres 0.5
```