{"data":{"full_name":"SarwanShah/Distracted-Pedestrian-Classification-Using-CNNs-2020","name":"Distracted-Pedestrian-Classification-Using-CNNs-2020","description":"Created a dataset of 1,300 images of distracted pedestrians and applied augmentation to expand it by 3x- 4x.  Used Faster R-CNN to localize pedestrians and extract distracted individuals.  Tested MLP, ANN (HOG), CNN, and VGG16 transfer learning, achieving 23%, 28%, 60%, and 62% accuracy, respectively.","stars":1.0,"forks":0.0,"language":"Jupyter Notebook","license":"MIT","archived":0.0,"subcategory":"driver-attention-monitoring","last_pushed_at":"2025-03-04T06:12:48+00:00","pypi_package":null,"npm_package":null,"downloads_monthly":0.0,"dependency_count":0.0,"commits_30d":null,"reverse_dep_count":0.0,"maintenance_score":0.0,"adoption_score":1.0,"maturity_score":9.0,"community_score":0.0,"quality_score":10.0,"quality_tier":"experimental","risk_flags":"['stale_6m', 'no_package', 'no_dependents']"},"meta":{"timestamp":"2026-04-08T12:39:30.312468+00:00"}}