{"id":30052008,"url":"https://github.com/ratikaewkam/physics-classification","last_synced_at":"2026-05-15T20:02:53.960Z","repository":{"id":307458094,"uuid":"1028409566","full_name":"ratikaewkam/physics-classification","owner":"ratikaewkam","description":"This project was developed as a prototype for future advanced video classification applications in schools or laboratories.","archived":false,"fork":false,"pushed_at":"2025-07-31T09:06:07.000Z","size":846,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":0,"default_branch":"main","last_synced_at":"2025-08-07T16:48:29.351Z","etag":null,"topics":["neural-networks","physics","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter 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model using two classification classes:\n\n#### Free Fall\n\n![Free Fall](https://raw.githubusercontent.com/ratikaewkam/physics-classification/refs/heads/main/img/Freefall.png)\n\n#### Sound vs Fire\n\n![Sound vs Fire](https://raw.githubusercontent.com/ratikaewkam/physics-classification/refs/heads/main/img/SoundvsFire.png)\n\nWe designed three different neural network models by varying the activation functions, the number of layers, and the number of neurons.\nAll three models achieved 100% accuracy, but the third model had the fewest parameters, making it the fastest in terms of computation and prediction speed.\n\n### Results\nWe divided the dataset into four folders: training, testing, validation, and unseen.\n- During training and testing, all models performed without any issues.\n- However, when we used unseen data—especially videos captured from different camera perspectives (e.g., different zoom levels)—the models struggled to make accurate predictions.\n- If the perspective and setup of the unseen video matched the training or testing conditions, the models could still predict correctly, even if that exact video had never been used before.\n\n### Next Steps\nIn the next phase, I will collaborate with three high school students. I will build the base model, and they will apply it in real-world scenarios. For example, they will integrate the model with a Raspberry Pi.\nAs for me, if time permits, I plan to explore new approaches to neural network design.\n\n### Credits\n- Model Development \u0026 Owner of the Sound vs. Fire Dataset: ([Rati Kaewkam](https://github.com/ratikaewkam))\n- Raspberry Pi Integration \u0026 Owners of the Free Fall Dataset: (Three high school students)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fratikaewkam%2Fphysics-classification","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fratikaewkam%2Fphysics-classification","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fratikaewkam%2Fphysics-classification/lists"}