# PyTorch-Simple-MaskRCNN

**Repository Path**: wuwu-wu/PyTorch-Simple-MaskRCNN

## Basic Information

- **Project Name**: PyTorch-Simple-MaskRCNN
- **Description**: 同步 https://github.com/Okery/PyTorch-Simple-MaskRCNN
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2023-02-28
- **Last Updated**: 2023-02-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# PyTorch-Simple-MaskRCNN

A PyTorch implementation of simple Mask R-CNN.

This repository is a toy example of Mask R-CNN with two features:
- It is pure python code and can be run immediately using PyTorch 1.4 without build
- Simplified construction and easy to understand how the model works

The code is based largely on [TorchVision](https://github.com/pytorch/vision), but simplified a lot and faster (1.5x).

## Requirements

- **Windows** or **Linux**, with **Python ≥ 3.6**

- **[PyTorch](https://pytorch.org/) ≥ 1.4.0**

- **matplotlib** - visualizing images and results

- **[pycocotools](https://github.com/cocodataset/cocoapi)** - for COCO dataset and evaluation; Windows version is [here](https://github.com/philferriere/cocoapi)

There is a problem with pycocotools for Windows. See [Issue #356](https://github.com/cocodataset/cocoapi/issues/356).

Besides, it's better to remove the prints in pycocotools.

## Datasets

This repository supports VOC and COCO datasets.

If you want to train your own dataset, you may:

- write the correponding dataset code

- convert your dataset to COCO-style

**PASCAL VOC 2012** ([download](http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar)): ```http://host.robots.ox.ac.uk/pascal/VOC/voc2012/```

**MS COCO 2017**: ```http://cocodataset.org/```

COCO dataset directory should be like this:
```
coco2017/
    annotations/
        instances_train2017.json
        instances_val2017.json
        ...
    train2017/
        000000000009.jpg
        ...
    val2017/
        000000000139.jpg
        ...
```

The code will check the dataset first before start, filtering samples without annotations.

## Training

```
python train.py --use-cuda --iters 200 --dataset coco --data-dir /data/coco2017
```
or modify the parameters in ```run.sh```, and run:
```
bash ./run.sh
```

Note: This is a simple model and only supports ```batch_size = 1```. 

The code will save and resume automatically using the checkpoint file.

## Evaluation

- Modify the parameters in ```eval.ipynb``` to test the model.

![example](https://github.com/Okery/PyTorch-Simple-MaskRCNN/blob/master/image/001.png)

## Performance

The model utilizes part of TorchVision's weights, which is pretrained on COCO dataset.

Test on VOC 2012 Segmentation val, on 1 RTX 2080Ti GPU:

| model | backbone | imgs/s (train) | imgs/s (test)|epoch | bbox AP | mask AP |
| ---- | ---- | --- | --- | -- | -- | -- |
| Mask R-CNN | ResNet 50 | 11.5 | 15.8 | 5 | 52.2 | 37.0 |