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Discover the power of YOLO-NAS, Deci's next-generation object detection model, in this comprehensive guide. We'll walk you through the Python setup, installing YOLO-NAS, running inferences with the pre-trained COCO model, and even training YOLO-NAS on your custom dataset. Dive into the superior real-time object detection capabilities of this game-changing model and learn how to use it to optimize your own AI projects.
Chapters:
00:00 Introduction and Model Overview
01:57 Python Environment Setup and Installing YOLO-NAS
04:56 Inference with pre-trained COCO model
07:21 YOLO-NAS Inference Output Format
09:06 Finding Open-source Datasets
10:19 Training YOLO-NAS on Custom Dataset
16:34 Evaluate Trained Model
17:28 Inference with Trained Model
18:39 Conclusion
Resources:
🌏 Roboflow: roboflow.com
🌌 Roboflow Universe: universe.roboflow.com
📚 Roboflow Notebooks Repository: github.com/roboflow/notebooks
📚 How to Train YOLO-NAS on a Custom Dataset blog post: blog.roboflow.com/yolo-nas-ho...
📓 How to Train YOLO-NAS on a Custom Dataset notebook: colab.research.google.com/git...
❓ Why do we need to restart Google Colab? github.com/obss/sahi/discussi...
🎬 YOLOv8: How to Train for Object Detection on a Custom Dataset: • YOLOv8: How to Train f...
Stay updated with the projects I'm working on at github.com/roboflow and github.com/SkalskiP! ⭐
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#YOLO_NAS #Deci #ObjectDetection #NeuralArchitectureSearch #Python #COCO #MachineLearning #ArtificialIntelligence #CustomDataset #Inference #ModelEvaluation #OpenSource #Datasets