# FCN_segmentation

**Repository Path**: tutu123456_admin/FCN_segmentation

## Basic Information

- **Project Name**: FCN_segmentation
- **Description**: A implementation of FCN segmentation project based with  tensorflow on VOC2011 dataset
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2018-09-27
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# FCN_segmentation

A TensorFlow Implementation of:

[CVPR 2015] Long et al. [Fully Convolutional Networks for Semantic Segmentation](https://people.eecs.berkeley.edu/~jonlong/long_shelhamer_fcn.pdf)

**NOTE:** In this repository, we only implement the VGG16 version.


## Requirements

1. TensorFlow 1.5.0 or newer

2. OpenCV 2 and its Python bindings

3. IPython: IPython environment debugger

## Prepare dataset

In this implementation, we use the [VOC2011 dataset](http://host.robots.ox.ac.uk/pascal/VOC/voc2011/). Please do as follows to set up the dataset:

1. `mkdir data` to set up the directory of dataset

2. Download the **train/val dataset** and **Development kit** tar files, put them under the `data` folder. Unzip Development kit tar file, then unzip train/val tar file and rename the folder as `VOC2011`.

3. It should have this basic structure (under `data` directory)

```bash
$ VOCdevkit/                      # development kit
$ VOCdevkit/VOCcode               # VOC utility code
$ VOCdevkit/VOC2011               # image sets, annotations, etc.
# ... and several other directories ...
```

You may also download the **test set** if you want to evaluate your prediction results on this dataset.

## Pre-trained model

```bash
mkdir model
```

We use a ImageNet pre-trained model to initialize the network, please download the npy file [here](https://drive.google.com/file/d/0B2SnTpv8L4iLRTFZb0FWenRJTlU/view?usp=sharing) and put it under the `model` folder.


## How to train

Since input images have different sizes, in order to make them as minibatch input, we used two different strategies: 1) padding to a large size; or 2) resize to a small size (256, 256)

```bash
cd src
python mul_train.py      # padding
python train_small.py    # resize
```

You can choose either one to run. And you can also change the `config` dictionary to use custom settings.


## Demo

```bash
cd src
python demo.py
```

You can change the `config` dictionary to use custom settings.


## Generate Predictions

First you should run the following code:

```bash
cd src
python test.py
```

You might want to change the used model. Check the code for futher details.

After that, you should find the following structure in `result` folder:

```bash
$ FCN8_adam_iter_10000/          # folder name depends on the model you used
$ FCN8_adam_iter_10000/gray/     # gray-scale segmentation result
$ FCN8_adam_iter_10000/rgb/      # rgb segmentation result
# ... and maybe several other directories ...
```

Then you can use the VOC2011 provided eval code to do evaluation (see the next section for details).

If you want to evaluate your model on the test split, you may submit your prediction results to [their server](http://host.robots.ox.ac.uk:8080/)


## Evaluation

1. `cd misc`

2. Run `save_colorful_grayscale(in_directory,out_directory)` (our generated results is grayscale png format, but the eval code uses indexed png format)

3. Run `report.m`

**Note:**

1. make sure `VOCinit.m` is at `/FCN_segmentation/data/VOCdevkit/VOCcode/`

2. make sure the segmentation result is stored in `/FCN_segmentation/data/VOCdevkit/results/VOC2011/Segmentation/%s_val_cls/` while the folder is named as `%s_val_cls`

3. make sure the second input of `Evaluation(VOCopts, ~)` is the string `%s` above.



## Models

Padding to (640, 640):

- FCN32_adam_20000: [ckpt](https://drive.google.com/file/d/0B3vJudZqxciYbTRuY21WZXREV0E/view?usp=sharing), [npy](https://drive.google.com/file/d/0B2SnTpv8L4iLNEVFd2RHcUZOX00/view?usp=sharing)

- FCN16_adam_5000:  [ckpt](https://drive.google.com/file/d/0B2SnTpv8L4iLT2VuREZwUHg4cjg/view?usp=sharing)

- FCN8_adam_10000:  [ckpt](https://drive.google.com/file/d/0B2SnTpv8L4iLRExqQTVONWxTX0U/view?usp=sharing)


Padding to (500, 500):

- FCN32_adam_35000: [ckpt](https://drive.google.com/file/d/0B3vJudZqxciYVWZfbXdybzFhWDA/view?usp=sharing) (You can extract npy with `extract` method defined in `Model.py`)

- FCN8_adam_30000: [ckpt](https://drive.google.com/file/d/0B3vJudZqxciYVWZfbXdybzFhWDA/view?usp=sharing)


**Note:** When you train the shortcut version model (FCN16 and FCN8), you will need FCN32 model npy file as initialization, instead of the ImageNet pre-trained model npy file.