You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
A deep learning project focused on building and improving automatic vegetable classification models using techniques like fine-tuning, augmentation, regularization, and transfer learning with AlexNet, ResNet-18, and EfficientNet-B0.
Split Flask/model-service produce platform with VegNet-23 and VegNet-101 packages, authentication/history/governance, 217 backend + 22 model tests, and ≥85% coverage gates.
11-class vegetable CNN study across four 23×23/101×101 augmentation regimes; several runs reach ~88%, while one depthwise run records ~94.2% test accuracy.