rubbish_detection

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rubbish_detection

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A rubbish detection application, based on a neural network built with keras and optimized with TensorFlow Lite API

rubbish_detection 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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# Rubbish detector

## Getting started

* Clone repository
```
git clone https://github.com/luca-ant/rubbish_detector.git
```

* Install dependencies
```
sudo apt install python3-setuptools
sudo apt install python3-pip
sudo apt install python3-venv
```

* Create a virtual environment and install requirements modules
```
cd rubbish_detector
python3 -m venv venv
source venv/bin/activate

python3 -m pip install -r requirements.txt
```

## Download dataset
Run the script ```download_dataset.py``` to download the dataset in the correct structure.

```
python download_dataset.py
```

## Configuration

Change the file ```config.py``` and uncomment the model that you prefer.

## Running

* **Training:** After choose the model in the config.py file, run the script ```train_rubbish_detector.py```. You'll find training history and data in ```training_logs``` directory.

```
python train_rubbish_detector.py
```

* **Evaluate:** To evaluate whole model on test images and calculate accuracy run the script ```evaluate_rubbish_detector.py```

```
python evaluate_rubbish_detector.py
```

* **Predict:** To use the classifier to predict a class for your image run the script ```predict_rubbish.py```

```
python predict_rubbish.py PATH_TO_YOUR_IMAGE 
```

To use the webcam of your pc as input use the script ```camera.py``` and press ```SPACE BAR``` to take a photo and predict the the class.

```
python camera.py
```

### Download already trained keras models
Download the zip archive containing all models and extract it in the main directory fo the repository. Use the following commands:

```
cd rubbish_detection
wget https://github.com/luca-ant/rubbish_detection/releases/download/models/models.zip
tar -xvf models_tflite.tgz
```

## Training results

| Keras model   | Accuracy |
| :---          |   :---:  |
| InceptionV3   |  94.95%  |
| MobileNetV2   |  92.55%  |
| NASNetMobile  |  93.62%  |
| ResNet50      |  94.68%  |

## Convert to Tensorflow Lite and optimize

To convert the model into tflite version run the script ```convert_keras_to_tflite.py```

```
python convert_keras_to_tflite.py
```

* **Evaluate:** To evaluate the non optimized tflite model run the script ```evaluate_rubbish_detector_lite.py```

```
python evaluate_rubbish_detector_lite.py
```

* **Predict:** To use the non optimized tflite model to predict a class for your image run the script ```predict_rubbish.py```

```
python predict_rubbish_lite.py PATH_TO_YOUR_IMAGE 
```

* **Test:** To test and compute accuracy of all optimized tflite models run the script ```test_tflite.py```. You'll find the output in ```test_results_tflite``` directory.

```
python test_tflite.py
```

### Download already converted and optimized tflite models
Download the tar archive containing all tflite models and extract it in the main directory fo the repository. Use the following commands:

```
cd rubbish_detection
wget https://github.com/luca-ant/rubbish_detection/releases/download/models_tflite/models_tflite.tgz
tar -xvf models_tflite.tgz
```

## Optimization results

| TFLite Model                          | Accuracy |
| :---                                  |   :---:  |
| InceptionV3                           |  94.95%  |
| InceptionV3 float16 quantization      |  94.95%  |
| InceptionV3 weights quantization      |  94.41%  |
| InceptionV3 integer quantization      |  94.41%  |
| MobileNetV2                           |  92.55%  |
| MobileNetV2 float16 quantization      |  92.55%  |
| MobileNetV2 weights quantization      |  34.04%  |
| MobileNetV2 integer quantization      |  92.02%  |
| NASNetMobile                          |  93.62%  |
| NASNetMobile float16 quantization     |  93.62%  |
| NASNetMobile weights quantization     |  92.82%  |
| NASNetMobile integer quantization     |  91.76%  |
| ResNet50                              |  94.68%  |
| ResNet50 float16 quantization         |  94.68%  |
| ResNet50 weights quantization         |  95.21%  |
| ResNet50 integer quantization         |  94.68%  |

## To be continued...
TensorFlow Lite models can run also on a smartphone such as Andorid or iOS. See [here](https://github.com/Fedeee9/rubbish_detection_app) for details!

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