YOLO-NAS: Introducing One of The Most Efficient Object Detection Algorithms

📚 Blog post Link: learnopencv.com/yolo-nas/
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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:
🖥️ On our blog - learnopencv.com we also share tutorials and code on topics like Image
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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🔖Hashtags🔖
#Yolo-NAS #Yolo #yolomodels #objectdetection #deeplearning #computervision #learnopencv #opencv #yolov8 #yolov7 #yolov5

[@jairajsahgal7101]

Thank you

[@user-th8id5vv7u]

you make very cool videos!

[@LearnOpenCV]

Thank you so much!

[@sandecomx]

awesome

[@LearnOpenCV]

Thank you!

[@iyshwaryakannan6677]

Hi sir, where can I find the YOLO NAS colab notebook to work on

[@LearnOpenCV]

Hi, you can find the notebook here: github.com/spmallick/learnopencv/tree/master/YOLO-NAS_Introduction

[@vishalmogal8839]

Hi Sir, Can you please make a video on YOLO world model? How it is different from other YOLO model with Use case for yoloworld model.

[@LearnOpenCV]

Yolo World is a zero shot object detection model with its YoloV8 backbone to extract image features along with a shared vocabulary embeddings prompted by the user. This avoids the need to manually annotate images unlike traditional Yolo models.So when a user prompts with the desired class it's converted to vocab embeds,the box head shared with text contrastive head helps to find the object embeddings fusing text and image features Earlier Vocab embed models are transformer based backbone requiring heavy compute and slow wherein Yolo backbone models are known for lighweight and fast inference. For more on this checkout: docs.ultralytics.com/models/yolo-world/

[@vishalmogal8839]

Thank you so much

[@readbhagwatgeeta3810]

Can anyone please how to save the model after training on custom dataset, so that I don't have to train again and again for inference in different type of videos

[@LearnOpenCV]

All the model checkpoints are saved in their respective "experiment-name" directories.

[@AmitKumar-hm4gx]

but how is a v5 model better than v7 ??

[@LearnOpenCV]

YOLOv5 and YOLOv7 have different authors and developers.

[@AmitKumar-hm4gx]

@@LearnOpenCV I thought v7 is also from ultralytics…

[@LearnOpenCV]

Check this repo for more info: github.com/WongKinYiu/yolov7

[@thiagarajamuralidaran1371]

Thank you