# RefineDet.PyTorch

**Repository Path**: gwmen_dl/RefineDet.PyTorch

## Basic Information

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

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-02-04
- **Last Updated**: 2024-02-04

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

A higher performance [PyTorch](http://pytorch.org/) implementation of [Single-Shot Refinement Neural Network for Object Detection](https://arxiv.org/abs/1711.06897 ). The official and original Caffe code can be found [here](https://github.com/sfzhang15/RefineDet).

### Table of Contents
- <a href='#performance'>Performance</a>
- <a href='#installation'>Installation</a>
- <a href='#datasets'>Datasets</a>
- <a href='#training-refinedet'>Train</a>
- <a href='#evaluation'>Evaluate</a>
- <a href='#todo'>Future Work</a>
- <a href='#references'>Reference</a>

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## Performance

#### VOC2007 Test

##### mAP (*Single Scale Test*)

| Arch | Paper | Caffe Version | Our PyTorch Version |
|:-:|:-:|:-:|:-:|
| RefineDet320 | 80.0% | 79.52% | 79.81% |
| RefineDet512 | 81.8% | 81.85% | 80.50% |

## Installation
- Install [PyTorch](http://pytorch.org/) by selecting your environment on the website and running the appropriate command.
  * Note: You should use at least PyTorch0.4.0
- Clone this repository.
  * Note: We currently only support Python 3+.
- Then download the dataset by following the [instructions](#datasets) below.
- We now support [Visdom](https://github.com/facebookresearch/visdom) for real-time loss visualization during training!
  * To use Visdom in the browser:
  ```Shell
  # First install Python server and client
  pip install visdom
  # Start the server (probably in a screen or tmux)
  python -m visdom.server
  ```
  * Then (during training) navigate to http://localhost:8097/ (see the Train section below for training details).
- Note: For training, we currently support [VOC](http://host.robots.ox.ac.uk/pascal/VOC/) and [COCO](http://mscoco.org/), and aim to add [ImageNet](http://www.image-net.org/) support soon.

## Datasets
To make things easy, we provide bash scripts to handle the dataset downloads and setup for you.  We also provide simple dataset loaders that inherit `torch.utils.data.Dataset`, making them fully compatible with the `torchvision.datasets` [API](http://pytorch.org/docs/torchvision/datasets.html).


### COCO
Microsoft COCO: Common Objects in Context

##### Download COCO 2014
```Shell
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/COCO2014.sh
```

### VOC Dataset
PASCAL VOC: Visual Object Classes

##### Download VOC2007 trainval & test
```Shell
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2007.sh # <directory>
```

##### Download VOC2012 trainval
```Shell
# specify a directory for dataset to be downloaded into, else default is ~/data/
sh data/scripts/VOC2012.sh # <directory>
```

## Training RefineDet
- First download the fc-reduced [VGG-16](https://arxiv.org/abs/1409.1556) PyTorch base network weights at:              https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
- By default, we assume you have downloaded the file in the `RefineDet.PyTorch/weights` dir:

```Shell
mkdir weights
cd weights
wget https://s3.amazonaws.com/amdegroot-models/vgg16_reducedfc.pth
```

- To train RefineDet320 or RefineDet512 using the train scripts `train_refinedet320.sh` and `train_refinedet512.sh`. You can manually change them as you want.

```Shell
./train_refinedet320.sh  #./train_refinedet512.sh
```

- Note:
  * For training, an NVIDIA GPU is strongly recommended for speed.
  * For instructions on Visdom usage/installation, see the <a href='#installation'>Installation</a> section.
  * You can pick-up training from a checkpoint by specifying the path as one of the training parameters (again, see `train_refinedet.py` for options)

## Evaluation
To evaluate a trained network:

```Shell
./eval_refinedet.sh
```

You can specify the parameters listed in the `eval_refinedet.py` file by flagging them or manually changing them.  

## TODO
We have accumulated the following to-do list, which we hope to complete in the near future
- Still to come:
  * [ ] Support for multi-scale testing

## References
- [Original Implementation (CAFFE)](https://github.com/sfzhang15/RefineDet)
- A list of other great SSD ports that were sources of inspiration:
  * [amdegroot/ssd.pytorch](https://github.com/amdegroot/ssd.pytorch)
  * [lzx1413/PytorchSSD](https://github.com/lzx1413/PytorchSSD)