Recently, we published a paper "Automated fruit recognition using EfficientNet and MixNet", can be found here: https://doi.org/10.1016/j.compag.2020.105326 or https://www.researchgate.net/publication/339798572_Automated_fruit_recognition_using_EfficientNet_and_MixNet

This is an neural network webapp visualizing the training of the network and testing accuracy ~ 99.5% accuracy. The neural network uses pretrained EfficientNet_B0 and then trained to classify images of fruits and vegetables. It is built using Pytorch framework using Python as primary language. The webapp is built using Flask.

120 Category Fruits and Vegetables Dataset can be found here:

https://www.kaggle.com/moltean/fruits or https://github.com/Horea94/Fruit-Images-Dataset And the original paper of the dataset was introduced by Horea Mureșan and Mihai Oltean (https://www.researchgate.net/publication/321475443_Fruit_recognition_from_images_using_deep_learning)

EfficientNet family was introduced by a paper from Google Brain at https://arxiv.org/pdf/1905.11946.pdf And codes for the EfficientNet family were hacked by Ross Wrightman. Thank Ross for his fantastic work to create valuable models for image classification tasks on PyTorch. We can find the codes of Ross here: https://github.com/rwightman/pytorch-image-models and https://github.com/rwightman/gen-efficientnet-pytorch

• You can download the trained weight of EfficientNet_B0 model here

• to reproduce our experiments in our paper, you can use [EfficientNet_B0_SGD.ipynb] (https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp/blob/master/EfficientNet_B0_SGD.ipynb)
• Input image is fed and transformed using : commons.py
• Inference is done by : inference.py
• Run on local web: [app.py] (https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp/blob/master/app.py)

Make sure you have installed Python , Pytorch, Flask and other related packages, refer requirement.txt.

• First download all the folders and files
  git clone https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp.git
• Then open the command prompt (or powershell) and change the directory to the path where all the files are located.
  cd Fruits_Vegetable_Classifier_WebApp
• Now run the following commands -

python app.py

This will firstly download the models and then start the local web server.

now go to the local server something like this - http:https://127.0.0.1:5000/ and see the result and explore.

If you find our work useful for your research, please cite the paper using the following BibTex entry:

@article{duong2020automated,
  title={Automated fruit recognition using EfficientNet and MixNet},
  author={Duong, Linh T and Nguyen, Phuong T and Di Sipio, Claudio and Di Ruscio, Davide},
  journal={Computers and Electronics in Agriculture},
  volume={171},
  pages={105326},
  year={2020},
  publisher={Elsevier}
}