deep-xgboost-image-classifier

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deep-xgboost-image-classifier

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# Deep XGBoost Image Classifier

## I HAVEN'T MAINTAINED THIS REPO IN SEVERAL YEARS, DO NOT EXPCET FOR IT TO WORK WITH CURRENT LIBRARIES

This project was developed Fall 2019

CNN+XGBoost architectures are very accurate in solving non-image classification problems. In this project, I adapt this model to show a shockingly powerful method for image classification. The idea is to leverage CNN's feature extraction capabilities and XGBoost's classification accuracy. I use the CIFAR-10 data set and I test the hybrid model on three different CNN architectures. A baseline CNN architecture, the VGG16 architecture, and the ResNet architecture.

## Getting Started

A list of all the prerequisites you'll need to run the experiments and the files the code will generate with the parameters to load the CNN and CNN+XGBoost models for each iteration.

### Prerequisites

```
Python
Keras
tensorflow
xgboost
sklearn
numpy
scipy
pickle
```

### Files Generated

For each CNN+XBoost the code will create files to load and evaluate the different trained models used in this project.

```
For each iteration:
model.json			/* CNN model */
model.h5			/* CNN model trained weight */
cnn_xgboost_final.pickle.dat	/* CNN+XGBoost model* /
```
To evaluate the accuracy of the CNN+XGBoost model I also developed other hybrid models based on other classification algorithms, CNN+SVM and CNN+kNN. For clarity and simplicity I uploaded to this repository the fully trained models as:
```
For each iteration:
cnn_SVM.pickle.dat		/* CNN+SVM model */
cnn_kNN.pickle.dat		/* CNN+kNN model */
```

## Running Experiments

For each iteration, I train the original CNN model, I used the train model to generate the CNN+XGBoost model and I compare the accoracy of each model. Download this repository and run the following code for each CNN+XGboost model:

### Baseline
```
cd code/baseline/
python3 cnn.py
python3 cnn_xgboost.py
python3 accuracy_baseline.py
```
### VGG16
```
cd code/vgg16/
python3 cnn_vgg16.py
python3 cnn_vgg16_xgboost.py
python3 acuoracy_vgg16.py
```
### ResNet50
```
cd code/resnet/
python3 cnn_resnet.py
python3 cnn_resnet_xgboost.py
python3 accuracy_resnet.py
```

## Results
### Baseline
| Model | Accuracy |		
| --- | --- |			
| CNN | 87.75%% |		
| CNN-SVM | 85.63% |		
| CNN-kNN | 83.54% |		
| CNN-XGBoost | **89.1%** |

### VGG16
| Model | Accuracy |
| --- | --- |
| CNN | **93.58%** |
| CNN-SVM | 90.24% |
| CNN-kNN | 89.16% |
| CNN-XGBoost | 93.35% |

### ResNet50
| Model | Accuracy |
| --- | --- |
| CNN | **98.9%** |
| CNN-SVM | 90.92% |
| CNN-kNN | 87.98% |
| CNN-XGBoost | 94.18% |

## Files
```
code ---|- baseline --|-- cnn.py
	|    	      |-- cnn_xgboost.py
	|    	      |-- accuracy_baseline.py
	|
	|- resnet ----|-- cnn_resnet.py
	|    	      |-- cnn_resnet_xgboost.py
	|    	      |-- accuracy_resnet.py
	|
	|- vgg16 -----|-- cnn_vgg.py
	     	      |-- cnn_vgg_xgboots.py
	     	      |-- accuracy_vgg16.py
```

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