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https://github.com/linggarm/poultry-meat-freshness-classification-with-transfer-learning-using-resnet
The utilization of the ResNet architecture and Transfer Learning methods to classify Poultry Meat Freshness images into two categories, namely Fresh and Rotten.
https://github.com/linggarm/poultry-meat-freshness-classification-with-transfer-learning-using-resnet
artificial-intelligence binary-classification cnn colab-notebooks computer-vision deep-learning fine-tuning imagenet machine-learning meat-classification numpy pandas poultry python residual-networks resnet scikit-learn supervised-learning tensorflow transfer-learning
Last synced: 7 days ago
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The utilization of the ResNet architecture and Transfer Learning methods to classify Poultry Meat Freshness images into two categories, namely Fresh and Rotten.
- Host: GitHub
- URL: https://github.com/linggarm/poultry-meat-freshness-classification-with-transfer-learning-using-resnet
- Owner: LinggarM
- License: mit
- Created: 2021-06-02T19:59:39.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2023-11-07T19:20:56.000Z (about 1 year ago)
- Last Synced: 2023-11-08T19:47:35.240Z (about 1 year ago)
- Topics: artificial-intelligence, binary-classification, cnn, colab-notebooks, computer-vision, deep-learning, fine-tuning, imagenet, machine-learning, meat-classification, numpy, pandas, poultry, python, residual-networks, resnet, scikit-learn, supervised-learning, tensorflow, transfer-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 89 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Poultry-Meat-Freshness-Classification-with-Transfer-Learning-using-ResNet-Architecture
The utilization of the ResNet architecture and **Transfer Learning** methods to classify **Poultry Meat Freshness images** into two categories, namely **Fresh** and **Rotten**.
## About The Project
* In this repository, we leverage the power of the **Residual Networks (ResNet)** architecture and **Transfer Learning** techniques to accurately classify the freshness of poultry meat. By utilizing pre-trained ResNet weights, sourced from **ImageNet**, our model gains the ability to make fine-grained distinctions in poultry meat freshness.
* The pre-trained weights from the **ImageNet** dataset, which includes a wide range of object categories, are used to enhance the model's ability to recognize and classify fresh and rotten poultry meat images.
* This repository provides a comprehensive implementation of the classification process and serves as a valuable resource for exploring the world of Transfer Learning with ResNet architecture.
* Key Features
* Implementation of Transfer Learning with ResNet architecture.
* Classification of poultry meat freshness into different categories.
* Utilizes a pre-trained model to enhance classification accuracy.
* Implement hyperparameter tuning using Grid Search to obtain the best model/ set of parameters.
* Helps in maintaining food safety and quality standards.
## Technology Used
* Python
* Numpy
* Pandas
* Matplotlib
* Scikit-learn
* Keras
* Tensorflow
## Dataset Used
- In this project, we utilize sets of images depicting fresh and spoiled poultry meat sourced from the **Fresh and rotten poultry meat datasets**, accessible at: [Fresh and rotten poultry meat datasets | Kaggle](https://www.kaggle.com/calvinsama/fresh-and-rotten-poultry-meat-datasets).
- The amount of data used in this project:
- Training data:
- Fresh (Segar): 500 images
- Rotten (Busuk): 500 images
- Testing data
- Fresh (Segar): 150 images
- Rotten (Busuk): 150 images
- Sample Images
- Fresh (Segar)
1 | 2 | 3 | 4 | 5
:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:
 |  |  |  | 
- Rotten (Busuk)
1 | 2 | 3 | 4 | 5
:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:|:-------------------------:
 |  |  |  | 
## Workflow
- Data Preparation
- Label Encoding
- Data Preprocessing & Data Augmentation
- Data Splitting
- Model Building
- Model Training & Hyperparameters Tuning
- Model Testing & Evaluation
## Algorithms/ Methods
* This project applies **Transfer Learning** methods by utilizing the **ResNet architecture** with pre-trained weights sourced from **ImageNet**, encompassing around 1000 object categories.
* The training process uses a **fine-tuned method**, which allows all of the layers to update their weights during the training process.
* **Parameters** (Fixed):
* Batch = 10 (100 steps per epoch, because the number of training data is 1000)
* Optimizer = Mini Batch Gradient Descent
* Loss/ Cost Function = Binary Cross Entropy
* Metrics = Accuracy
* **Hyperparameters** (Fine Tuned with Grid Search):
* Epoch:
* 100
* 200
* Learning rate:
* 0.0001
* 0.0003
* 0.0007
* Momentum:
* 0.0
* 0.9
## Model Evaluation
Graph of Epoch & Accuracy (100 epoch) | Graph of Epoch & Accuracy (200 epoch)
:-------------------------:|:-------------------------:
 | 
Graph of Loss & Accuracy (100 epoch) | Graph of Loss & Accuracy (200 epoch)
 | 
- Validation accuracy scores at **Epoch 100**:
- resnet_lr1e-4_m0_e100: 92.33
- resnet_lr3e-4_m0_e100: **95.33**
- resnet_lr7e-4_m0_e100: 94.67
- resnet_lr1e-4_m09_e100: 93.67
- resnet_lr3e-4_m09_e100: 92.67
- resnet_lr7e-4_m09_e100: 92.0
- Validation accuracy scores at **Epoch 200**:
- resnet_lr1e-4_m0_e200: 93.0
- resnet_lr3e-4_m0_e200: 95.0
- resnet_lr7e-4_m0_e200: **95.67**
- resnet_lr1e-4_m09_e200: 91.0
- resnet_lr3e-4_m09_e200: 91.67
- resnet_lr7e-4_m09_e200: 90.67
## Publication
* [Cendani, L. M., Pangestu, M. A., & Muria, F. (2021). "*Classification of Freshness Quality of Broiler Chicken Breast with Transfer Learning Using ResNet, Inception, and Xception Architectures*".](paper/Klasifikasi%20Kualitas%20Kesegaran%20Daging%20Dada%20Ayam%20Broiler%20dengan%20Transfer%20Learning%20M.pdf) (unpublished)
## Contributors
* [Linggar Maretva Cendani](https://github.com/LinggarM) - [[email protected]](mailto:[email protected]) (ResNet)
* Michael Axel Pangestu (Inception)
* Fatah Muria (Xception)
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details
## Acknowledgments
- Fresh and rotten poultry meat datasets: [https://www.kaggle.com/calvinsama/fresh-and-rotten-poultry-meat-datasets](https://www.kaggle.com/calvinsama/fresh-and-rotten-poultry-meat-datasets).