YOLOv10 introduces a dual-head architecture for NMS-free training and efficiency-accuracy driven model design. It combines one-to-one and one-to-many label assignments to improve performance without extra computation. YOLOv10 uses lightweight classification heads, spatial-channel decoupled downsampling, and rank-guided blocks. This results in faster, more accurate object detection, making it ideal for real-time applications.
Ankan Ghosh
June 4, 2024
This research article discusses about how data preparation matters for Fine-tuning Faster R-CNN on aerial small object detection.
Jaykumaran
May 28, 2024
Recommendation systems (recommender systems) suggest content based on user preferences and behaviors. This guide explores their types, traditional ML techniques like matrix factorization, and advanced deep learning methods like neural collaborative filtering. Discover how these systems enhance user experiences on platforms like Netflix, Spotify, and Amazon.
Ankan Ghosh
May 21, 2024
This article presents ASR with Diarization using OpenAI Whisper and Nvidia Nemo Toolkit.
Jaykumaran
May 14, 2024
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