🧠🌿 Artificial Neural Networks & Deep Learning Project

Image Classification

• 📙 Description
• 🗄️ Dataset
• ⚙ System requirements️
• 🚀 Setup instructions
• 📜 Report
• 🤵 Authors
• 📝 License

📙 Description

The Politecnico di Milano is hosting its first challenge for the Artificial Neural Networks and Deep Learning course of 2022, and this repository serves as the platform for it. Participants will be tasked to classify various species of plants, divided into 8 categories based on their plant species. The objective of this challenge is to accurately predict the correct class label when presented with an image, making it a classification problem.

The model me and my team developed uses the transfer learning approach with ConvNeXtLarge as the base model.

Our team arrived second in the challenge, reaching an accuracy of 94.46%, you can check the results here

🗄️ Dataset

You can find the dataset here.

The dataset provided is structured in a single folder containing the following classes:

• Species1 : 186 images
• Species2 : 532 images
• Species3 : 515 images
• Species4 : 511 images
• Species5 : 531 images
• Species6 : 222 images
• Species7 : 537 images
• Species8 : 508 images

The dataset contains in total 3542 images of size 96x96

Examples

Species 1	Species 2	Species 3	Species 4	Species 5	Species 6	Species 7	Species 8

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Models details

For more details about the model check the Report. Briefly, we used the following techniques:

1. Transfer Learning with ConvNeXtLarge model.
2. CutMix & MixUp & CutOut data augmentation techniques: one of these techniques was applied to each input image
3. Standard data augmentation (flip, rotate, zoom)
4. Test-Time data augmentation (self-ensemble technique) with shifts and flips
5. Class weighting to fight the imbalance of the dataset

Results

Our best model reached an accuracy of 95% on the validation set and 94.46% accuracy in the hidden test provided by the challenge host.
F1-score table on the test set:

	Species 1	Species 2	Species 3	Species 4	Species 5	Species 6	Species 7	Species 8
F1-score	0.8909	0.9477	0.9753	0.9451	0.9526	0.9535	0.9790	0.9130

Final position: 2 out of 300.

⚙ System requirements

Required software

• Jupyter Notebook or Google Colab
• Python modules in requirements.txt

🚀 Setup instructions

Clone:

git clone https://github.com/gccianmario/Online-learning-application-projects/
pip install -r requirements.txt

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🤵 Authors

Surname	Name	Contact Info
Breda	Leonardo	leonardo.breda@mail.polimi.it
Giaccaglia	Pablo	pablo.giaccaglia@mail.polimi.it
La Conca	Alessandro	alessandro.laconca@mail.polimi.it

📝 License

This file is part of "Artificial Neural Networks & Deep Learning Project".

"Artificial Neural Networks & Deep Learning Project" is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

"Artificial Neural Networks & Deep Learning Project" is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program (LICENSE.txt). If not, see http://www.gnu.org/licenses/