Strengths4
- End-to-end functionality from training to serving.
- Clear and detailed documentation with diagrams.
- Modular code organization separating concerns.
- Use of experiment tracking (wandb) and reproducibility practices.
ML Application · Python
face-auth-pipeline
A real-time face verification system built with PyTorch and FastAPI, featuring a custom Hybrid Triplet Loss with margin scheduling, ResNet-50 backbone, MTCNN detection, and W&B integration for training insights. Supports image embedding extraction and API-based face comparison.
What it is
The repository provides an end-to-end face verification system that includes a training pipeline for fine-tuning a ResNet-50 model with a hybrid triplet loss and a FastAPI service for face detection and verification. It allows users to train on their own data and deploy a model for access control with three-tiered verification outcomes.
Merit describes the code at this commit. Later pushes count once the owner rescans. How merit is scored
Where it counts
- Machine Learning90%
- Computer Vision80%
- MLOps30%
- Data Science20%
How much of the project is about each field, as scoring counts it. It ranks in the field it is most about.
What the judge found
Read from the project's code at its scored commit.
Limitations
The owner keeps this project's limitations private.
Around it on the Machine Learning board
Whole board- 86AgroADASML Application · Python52.6ImplementationSuceveanu Mircea Dan SanduMachine Learning
- 87open-i3Architecture Modification / Research · Python52.5ImplementationFlameF0XMachine Learning
- 88, shared rankStudent_score_predictionML Application · Jupyter Notebook52.1Implementationkamran akmalMachine Learning
- 88, shared rankface-auth-pipelineML Application · Python52.1ImplementationMohamed KhayatMachine Learning
- 90, shared rankml-from-scratchEducational Implementation · Jupyter Notebook51.9ImplementationPop AlexandruMachine Learning
