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YOLO-NAS: Introducing One of The Most Efficient Object Detection Algorithms
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What is Yolo-NAS?
YOLO-NAS is an object detection model developed by Deci and delivers superior real-time object detection capabilities and high performance ready for production. YOLO-NAS models outperformed models like YOLOv7, and YOLOv8, including the recently launched YOLOv6-v3.0.
YOLO NAS performs a mighty 20.5% better than YOLOV7, a little more than 11% over YOLOV5 and a teeny 1.75% improvement over YOLOV8.
This video talks about Yolo-NAS and shows the object detection and instance segmentation prediction results on a video using the Yolo-NAS model.
Topics Covered
✅What is YOLO-NAS?
✅Some Key Architectural Insights into YOLO-NAS
✅A Brief Summary Training of YOLO-NAS Models
✅How To Use YOLO-NAS For Inference
⭐️ Time Stamps:⭐️
00:00-00:22: Introduction
00:22-01:20: YOLO-NAS
01:20-01:32: Models
01:32-02:25: Inference
02:25-03:07: Libraries
03:07-03:25: Model Architecture
03:25-04:30: Running Inference
04:30-04:44: Results
04:44-06:02: NAS in YOLO-NAS
06:02-06:18: Outro
Resources:
Processing, Image Classification, Object Detection, Face Detection, Face Recognition, YOLO, Segmentation, Pose Estimation, and many more using OpenCV(Python/C++), PyTorch, and TensorFlow.
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