{"id":19965485,"url":"https://github.com/rafaykhattak/digit-classification-pytorch","last_synced_at":"2025-09-19T17:31:10.155Z","repository":{"id":171708802,"uuid":"648300106","full_name":"RafayKhattak/Digit-Classification-Pytorch","owner":"RafayKhattak","description":"Simple MNIST Handwritten Digit Classification using Pytorch","archived":false,"fork":false,"pushed_at":"2023-06-01T17:14:26.000Z","size":1344,"stargazers_count":8,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2024-11-13T02:32:20.042Z","etag":null,"topics":["digit-classification","neural-network","pytorch"],"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/RafayKhattak.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":"2023-06-01T16:44:40.000Z","updated_at":"2024-08-09T02:29:34.000Z","dependencies_parsed_at":null,"dependency_job_id":"1d28dcd4-c971-46d4-843d-347d3bd4bbc0","html_url":"https://github.com/RafayKhattak/Digit-Classification-Pytorch","commit_stats":null,"previous_names":["rafaykhattak/digit-classification-pytorch"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RafayKhattak%2FDigit-Classification-Pytorch","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RafayKhattak%2FDigit-Classification-Pytorch/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RafayKhattak%2FDigit-Classification-Pytorch/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/RafayKhattak%2FDigit-Classification-Pytorch/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/RafayKhattak","download_url":"https://codeload.github.com/RafayKhattak/Digit-Classification-Pytorch/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":233581133,"owners_count":18697588,"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":["digit-classification","neural-network","pytorch"],"created_at":"2024-11-13T02:28:56.967Z","updated_at":"2025-09-19T17:31:04.752Z","avatar_url":"https://github.com/RafayKhattak.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# MNIST Handwritten Digit Classification with PyTorch\nThis project demonstrates a simple implementation of a deep learning model for classifying handwritten digits from the MNIST dataset using the PyTorch library. The MNIST dataset is a widely-used benchmark dataset in the field of computer vision.\n## Project Overview\nThe goal of this project is to train a convolutional neural network (CNN) model to accurately classify handwritten digits from the MNIST dataset. The model is built using PyTorch, a popular deep learning framework, and trained using the Adam optimizer.\n\nThe project involves the following steps:\n- Loading and preprocessing the MNIST dataset\n- Designing and building a CNN model architecture\n- Training the model on the training data\n- Evaluating the model's performance on the test data\n- Saving and loading the trained model\n- Performing inference on new images\n## Requirements\n- Python (3.x)\n- PyTorch (1.x)\n- torchvision\n- PIL\n## Installation\n1. Clone the repository:\n```\ngit clone https://github.com/your-username/mnist-classification-pytorch.git\n```\n2. Install the required dependencies:\n```\npip install -r requirements.txt\n```\n## Usage\n1. Prepare the dataset:\n- The MNIST dataset will be automatically downloaded and preprocessed during the first run of the script. However, if you want to specify a different data directory or adjust any preprocessing parameters, you can modify the configuration in the script.\n2. Train the model:\n- Run the training script to train the model. You can adjust hyperparameters such as the number of epochs, learning rate, and batch size in the script.\n3. Evaluate the model:\n- After training, the model's performance on the test set will be evaluated automatically, and the accuracy score will be displayed.\n4. Perform inference:\n- You can use the trained model to make predictions on new images by running the inference script and providing the path to the image file.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frafaykhattak%2Fdigit-classification-pytorch","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frafaykhattak%2Fdigit-classification-pytorch","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frafaykhattak%2Fdigit-classification-pytorch/lists"}