https://github.com/rahman-samiur/flower-species-recognition

Using state-of-the-art pre-trained Deep Neural Net architectures for Flower Species Recognition
https://github.com/rahman-samiur/flower-species-recognition

cnn-for-visual-recognition python pytorch

Last synced: 9 days ago
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Using state-of-the-art pre-trained Deep Neural Net architectures for Flower Species Recognition

• Host: GitHub
• URL: https://github.com/rahman-samiur/flower-species-recognition
• Owner: rahman-samiur
• Created: 2024-08-17T10:20:29.000Z (about 2 months ago)
• Default Branch: main
• Last Pushed: 2024-08-17T11:27:35.000Z (about 2 months ago)
• Last Synced: 2024-09-26T20:04:15.094Z (9 days ago)
• Topics: cnn-for-visual-recognition, python, pytorch
• Language: Python
• Homepage:
• Size: 51.2 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Flower Species Recognition System #

### Summary of the project ###

* Pretrained state-of-the-art neural networks are used on *University of Oxford's* **FLOWERS17** and **FLOWERS102** dataset.

* Models used     - **Xception, Inception-v3, OverFeat, ResNet50, VGG16, VGG19**.

* Weights used    - **ImageNet**

* Classifier used - **Logistic Regression**

### Dependencies ###

* Theano or TensorFlow `sudo pip install theano` or `sudo pip install tensorflow`

* Keras `sudo pip install keras`

* NumPy `sudo pip install numpy`

* matplotlib `sudo pip install matplotlib` and you also need to do this `sudo apt-get install python-dev`

* seaborn `sudo pip install seaborn`

* h5py `sudo pip install h5py`

* scikit-learn `sudo pip install scikit-learn`

### System requirements

* This project used Windows 10 for development purposes and Odroid-XU4 for testing purposes.

### Usage ###

* Organize dataset                      - `python organize_flowers17.py`

* Feature extraction using CNN          - `python extract_features.py`

* Train model using Logistic Regression - `python train.py`

### Show me the numbers ###

The below tables shows the accuracies obtained for every Deep Neural Net model used to extract features from **FLOWERS17** dataset using different parameter settings.

* Result-1

  

  * test_size  : **0.10**

  * classifier : **Logistic Regression**

  

| Model        | Rank-1 accuracy | Rank-5 accuracy |

|--------------|-----------------|-----------------|

| Xception     | 97.06%      	 | 99.26%      	   |

| Inception-v3 | 96.32%          | 99.26%          |

| VGG16        | 85.29%          | 98.53%          |

| VGG19        | 88.24%          | 99.26%          |

| ResNet50     | 56.62%          | 90.44%          |

| MobileNet     | 98.53%          | 100.00%         |

| Inception
ResNetV2     | 91.91%          | 98.53%          |

* Result-2

  

  * test_size  : **0.30**

  * classifier : **Logistic Regression**

| Model        | Rank-1 accuracy | Rank-5 accuracy |

|--------------|-----------------|-----------------|

| Xception     | 93.38%          | 99.75%          |

| Inception-v3 | 96.81%          | 99.51%          |

| VGG16        | 88.24%          | 99.02%          |

| VGG19        | 88.73%          | 98.77%          |

| ResNet50     | 59.80%          | 86.52%          |

| MobileNet     | 96.32%         | 99.75%         |

| Inception
ResNetV2     | 88.48%          | 99.51%          |