{"id":24828180,"url":"https://github.com/soroushesnaashari/rice-images-classification-with-cnn-using-tensorflow","last_synced_at":"2026-04-13T01:39:42.789Z","repository":{"id":272294689,"uuid":"914945064","full_name":"soroushesnaashari/Rice-Images-Classification-with-CNN-using-TensorFlow","owner":"soroushesnaashari","description":"A Convolutional Neural Network (CNN) project using \"TensorFlow\" framework to classify Rice images into five types","archived":false,"fork":false,"pushed_at":"2025-03-03T20:22:46.000Z","size":2437,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-03-03T21:26:53.949Z","etag":null,"topics":["cnn","keras","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/soroushesnaashari.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-01-10T16:24:19.000Z","updated_at":"2025-03-03T20:22:49.000Z","dependencies_parsed_at":"2025-03-03T21:24:09.035Z","dependency_job_id":"f0faccfb-6ec5-4ead-ba6e-ed51dcabff88","html_url":"https://github.com/soroushesnaashari/Rice-Images-Classification-with-CNN-using-TensorFlow","commit_stats":null,"previous_names":["soroushesnaashari/rice-images-classification-with-cnn-using-tensorflow"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soroushesnaashari%2FRice-Images-Classification-with-CNN-using-TensorFlow","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soroushesnaashari%2FRice-Images-Classification-with-CNN-using-TensorFlow/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soroushesnaashari%2FRice-Images-Classification-with-CNN-using-TensorFlow/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/soroushesnaashari%2FRice-Images-Classification-with-CNN-using-TensorFlow/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/soroushesnaashari","download_url":"https://codeload.github.com/soroushesnaashari/Rice-Images-Classification-with-CNN-using-TensorFlow/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":245568920,"owners_count":20636864,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["cnn","keras","tensorflow"],"created_at":"2025-01-30T22:51:37.977Z","updated_at":"2026-04-13T01:39:42.773Z","avatar_url":"https://github.com/soroushesnaashari.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Rice Classification\n[![](Image.jpg)](https://unsplash.com/photos/aerial-view-photography-of-rice-crops-during-daytime-cusz0Bg-5mQ)\n\n### Overview\nThis project demonstrates how to build and train a Convolutional Neural Network (CNN) using TensorFlow to classify images of rice. The goal is to develop an automated image classification system that can accurately distinguish between different types of rice, a task that can be crucial in agricultural research and quality control. The project walks through data preprocessing, model design, training, evaluation and result visualization.\n\n\u003cbr\u003e\n\n### Project Workflow\nThe project follows an end-to-end workflow:\n1. **Data Acquisition \u0026 Preparation**\n   - ***Dataset Collection:*** Gather rice images from various sources.\n   - ***Data Preprocessing:*** Resize images, normalize pixel values, and (if needed) apply data augmentation to increase dataset diversity.\n\n2. **Model Design \u0026 Implementation**\n   - ***CNN Architecture:*** Develop a CNN model using TensorFlow. The model typically includes several convolutional layers, pooling layers, and dense layers to extract features and        perform classification.\n   - ***Compilation:*** Set up the model with appropriate loss functions and optimizers.\n\n3. **Training \u0026 Evaluation**\n   - ***Training:*** Train the model on the prepared dataset while monitoring performance on a validation set.\n   - ***Evaluation:*** Assess the model using metrics such as accuracy, loss curves, and confusion matrices to understand its strengths and weaknesses.\n\n4. **Results Visualization \u0026 Analysis**\n   - Plot training/validation curves to visualize the learning process.\n   - Display sample predictions along with actual labels to evaluate performance qualitatively.\n\n\u003cbr\u003e\n\n### Key Features\n- **End-to-End Pipeline:** From data loading and preprocessing to model training and evaluation.\n- **Custom CNN Architecture:** Designed specifically for rice image classification.\n- **TensorFlow Integration:** Utilizes TensorFlow’s high-level APIs for model building and training.\n- **Data Augmentation:** Techniques implemented (if applicable) to improve model robustness by artificially expanding the dataset.\n- **Comprehensive Evaluation:** Detailed analysis of model performance with metrics and visualizations.\n\n\u003cbr\u003e\n\n### Results\n- **Model Performance:** The trained CNN achieves competitive accuracy in classifying rice images (e.g., reaching an accuracy of **`over 99% on both models`** on the training set).\n- **Visual Insights:** Training and validation loss/accuracy curves are generated to monitor overfitting and learning progress.\n- **Error Analysis:** Confusion matrices and misclassified examples provide insight into the model's decision-making and help guide future improvements.\n\n\u003cbr\u003e\n\n### Repository Contents\n- **`rice_classification.ipynb`**: Jupyter Notebook with full code, visualizations, and explanations.\n- **`Data`:** Contains the [Original Dataset](https://www.kaggle.com/datasets/muratkokludataset/rice-image-dataset/data) and you can see the cleaned dataset in notebook.\n- **`README.md`:** Project documentation.\n\n\u003cbr\u003e\n\n### How to Contribute\nContributions are welcome! If you'd like to improve the project or add new features:\n\n1. **Fork the repository.**\n2. **Create a new branch.**\n3. **Make your changes and submit a pull request.**\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoroushesnaashari%2Frice-images-classification-with-cnn-using-tensorflow","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsoroushesnaashari%2Frice-images-classification-with-cnn-using-tensorflow","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsoroushesnaashari%2Frice-images-classification-with-cnn-using-tensorflow/lists"}