# CCNN-Tensorflow

**Repository Path**: ling_zhizzz/CCNN-Tensorflow

## Basic Information

- **Project Name**: CCNN-Tensorflow
- **Description**: Learning from scratch a confidence measure
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-04-05
- **Last Updated**: 2020-12-19

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# CCNN-Tensorflow

Tensorflow implementation of confidence estimation using a convolutional neural network

**Learning from scratch a confidence measure**  
[Matteo Poggi](https://vision.disi.unibo.it/~mpoggi/) and [Stefano Mattoccia](https://vision.disi.unibo.it/~smatt/Site/Home.html)   
BMVC 2016


![Alt text](https://github.com/fabiotosi92/CCNN-Tensorflow/blob/master/images/disparity/ad-census/000020_LR.png?raw=true "disparity")
![Alt text](https://github.com/fabiotosi92/CCNN-Tensorflow/blob/master/output/CCNN/ad-census/000020_LR.png?raw=true "confidence CCNN")


For more details:  
[project page](https://vision.disi.unibo.it/~mpoggi/code.html)  
[pdf](https://vision.disi.unibo.it/~mpoggi/papers/bmvc2016.pdf)  

## Requirements
This code was tested with Tensorflow 1.4, CUDA 8.0 and Ubuntu 16.04.  

## Training

Training takes about 15 minutes with the default parameters on 20 images of **KITTI 2012** on a single 1080Ti GPU card. 

```shell
python ./model/main.py --isTraining True --epoch 14 --batch_size 64 --patch_size 9 --dataset_training ./utils/kitti_training_set.txt --initial_learning_rate 0.003 --log_directory ./log --save_epoch_freq 2 --model_name CCNN.model 
```

**Warning:** appropriately change of "./utils/kitti_training_set.txt" is necessary to train from scratch the network. To this aim, it's provided a shell script to generate a new training file. 

```shell
./utils/kitti_generate_file.sh [path_disparities] [path_kitti_groundtruth] [index_from] [index_to] [output_file]
```

## Testing 

Test takes about 0.07 seconds on a single image of **KITTI 2012**  using a 1080Ti GPU card. 

```shell
python ./model/main.py --isTraining False --batch_size 1 --dataset_testing ./utils/kitti_testing_set.txt --checkpoint_path ./log/CCNN.model-595140 --output_path ./output/CCNN/ad-census/

```
**Warning:** you can test the network simply using "./utils/kitti_testing_set.txt" test file with images provided in "./images" folder. If you want to predict confidence estimations with other disparity maps use the shell script to generate a new testing file. 

```shell
./utils/kitti_generate_file.sh [path_disparities] [index_from] [index_to] [output_file]

```

## Models

You can download a pre-trained model in ./log

The model was trained for 14 epochs, a batch size of 64, an initial learning rate of 0.003 (reduced to 0.0003 after 10 epochs) and patches of 9x9 extracted from 20 disparity maps computed with AD-CENSUS algorithm on 000000_10..000019_10 stereo pairs of KITTI 12.

## Results

AUC comparison between Torch and Tensorflow implementations using disparity maps computed by AD-CENSUS algorithm on 174 testing images of **KITTI 2012**

**Optimal AUC**: 0.1073  
**Torch implementation AUC** (https://vision.disi.unibo.it/~mpoggi/code.html): 0.1230  
**Tensorflow implementation AUC**: 0.1222

![Alt text](https://github.com/fabiotosi92/CCNN-Tensorflow/blob/master/output/auc.png?raw=true "auc")