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flowersnet

Implementation for the flower species classification using CNN

https://github.com/yusufbrima/flowersnet

Science Score: 26.0%

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    Low similarity (10.3%) to scientific vocabulary
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Repository

Implementation for the flower species classification using CNN

Basic Info
  • Host: GitHub
  • Owner: yusufbrima
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 101 MB
Statistics
  • Stars: 1
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created almost 3 years ago · Last pushed almost 3 years ago
Metadata Files
Readme Citation

README.md

Model Training and Inference

This repository contains code for training and inference using a deep convolutional neural network model.

Introduction

This project aims to train and evaluate a deep learning model for a flower species classification (17 classes) task. The model is trained on a labeled dataset and later used to predict labels for new unseen flower images.

Installation

  • Clone this repository:

bash git clone git@github.com:yusufbrima/FlowersNet.git

  • Install the required packages:

bash cd your_repository pip install -r requirements.txt

Data Preparation

  • Download the dataset from here and extract it a directory which should be set in the base_dir variables in train.py,predict.py and Play.ipynb files.

Data Samples

Data Samples

Model Training

  • Train the model using the following command:

bash python train.py

Train and validation plots

Data Samples

Data Samples

Model Inference

  • Use the model to predict labels for new data using the following command:

bash python predict.py - Alternatively, you can use the Play.ipynb notebook to run the model inference and visualize the results.

  • The model weights are saved in the Models directory.

  • Train, validation, and test data are saved in the Results directory.

Model Performance

This table shows the performance metrics of the model on the training, validation, and test datasets.

| Dataset | Accuracy | Loss | |-----------|----------|---------| | Training | 1.00 | 0.07 | | Validation| 0.99 | 0.07 | | Test | 0.94 | 0.32 |

Sample Prediction Outputs

Sample Output

Sample Output

Sample Output

License

This project is licensed under the terms of the MIT license.

Owner

  • Name: Yusuf Brima
  • Login: yusufbrima
  • Kind: user
  • Location: Osnabrück, Germany

Deep Representation Learning | Mathematical Causal Inference| Modelling | Computational Entrepreneurship

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Dependencies

requirements.txt pypi
  • Jinja2 ==3.1.2
  • Markdown ==3.4.3
  • MarkupSafe ==2.1.2
  • Pillow ==9.4.0
  • PyQt3D ==5.15.5
  • PyQt5 ==5.15.7
  • PyQt5-sip ==12.11.0
  • PyQt6 ==6.5.0
  • PyQt6-3D ==6.5.0
  • PyQt6-Charts ==6.5.0
  • PyQt6-DataVisualization ==6.5.0
  • PyQt6-NetworkAuth ==6.5.0
  • PyQt6-WebEngine ==6.5.0
  • PyQt6-sip ==13.5.0
  • PyQtChart ==5.15.6
  • PyQtDataVisualization ==5.15.5
  • PyQtNetworkAuth ==5.15.5
  • PyQtPurchasing ==5.15.5
  • PyQtWebEngine ==5.15.6
  • QScintilla ==2.14.0
  • TBB ==0.2
  • Werkzeug ==2.2.3
  • absl-py ==1.4.0
  • cachetools ==5.3.0
  • certifi ==2022.12.7
  • cffi ==1.15.1
  • charset-normalizer ==3.1.0
  • filelock ==3.10.0
  • google-auth ==2.17.2
  • google-auth-oauthlib ==1.0.0
  • grpcio ==1.53.0
  • h5py ==3.8.0
  • html5lib ==1.1
  • idna ==3.4
  • mpmath ==1.3.0
  • networkx ==3.0
  • numpy ==1.24.2
  • oauthlib ==3.2.2
  • pandas ==2.0.0
  • ply ==3.11
  • protobuf ==4.22.1
  • pyasn1 ==0.4.8
  • pyasn1-modules ==0.2.8
  • pycparser ==2.21
  • python-dateutil ==2.8.2
  • pytz ==2023.3
  • requests ==2.28.2
  • requests-oauthlib ==1.3.1
  • rsa ==4.9
  • sip ==6.7.9
  • six ==1.16.0
  • soundfile ==0.12.1
  • sympy ==1.11.1
  • tensorboard ==2.12.1
  • tensorboard-data-server ==0.7.0
  • tensorboard-plugin-wit ==1.8.1
  • torch ==2.0.0
  • torch-summary ==1.4.5
  • torch-tb-profiler ==0.4.1
  • torchaudio ==2.0.1
  • torchsummary ==1.5.1
  • torchvision ==0.15.1
  • typing_extensions ==4.5.0
  • tzdata ==2023.3
  • urllib3 ==1.26.15
  • webencodings ==0.5.1