Fast labeling application with automatic labeling, thanks to the help of a pre-trained model.
-
Updated
Jun 10, 2024 - Python
Fast labeling application with automatic labeling, thanks to the help of a pre-trained model.
RiftGuru uses hybrid rule-based and ML-augmented detection to identify clutch moments in League of Legends matches. <------------------------------------------>Built for the AWS AI Hackathon 2025 | Processes Riot API data through a serverless pipeline to generate AI-powered narratives of your best plays.
End-to-end NLP pipeline for crypto sentiment: Features async news ingestion, multi-agent LLM data labeling, tokenizer vocabulary expansion, and custom FinBERT fine-tuning to capture domain-specific vernacular.
This vehicle identification project utilizes the YOLOv5 deep learning model for detecting and classifying vehicles from images, videos, and live streams. It supports real-time inference, saving outputs with bounding boxes, confidence scores, and class labels, making it ideal for traffic monitoring and smart surveillance systems.
Add a description, image, and links to the automated-labelling topic page so that developers can more easily learn about it.
To associate your repository with the automated-labelling topic, visit your repo's landing page and select "manage topics."