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Fastest YOLOv5 CPU Inference with Sparsity and DeepSparse with Mark Kurtz
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Discover the fastest CPU inference for YOLOv5 models with Mark Kurtz, Director of Machine Learning at Neural Magic! 🚀 In this video, Mark delves into the world of sparsification and its transformative impact on YOLOv5 models. Learn about Neural Magic's DeepSparse engine and how it brings GPU-class performance to commodity CPUs.
Key topics covered:
- Introduction to sparsification: algorithms, techniques, and motivations
- Performance improvements with YOLOv5 sparsification
- Sparse transfer learning and project-based sparsification
- Deploying models with the DeepSparse inference engine
- Exporting models to ONNX format and running with DeepSparse
- Quantization techniques for reducing precision and compute needs
Discover the impressive results of sparsification, including up to 7x faster latency, 12x faster throughput, and 13x smaller file sizes. Mark explains how to apply sparse transfer learning to fine-tune models on custom datasets, keeping architectures efficient while maintaining high accuracy.
Don't miss out on the next steps, including updates to YOLOv5 and YOLOv5 P6 models and upcoming research on knowledge distillation techniques. This video is a must-watch for anyone looking to optimize and deploy YOLOv5 models on CPUs.
🔗 Explore more:
Join our community and stay updated with the latest in AI and computer vision. Like, subscribe, and visit our links for more insights!
#YOLOv5 #Sparsification #DeepSparse #NeuralMagic #Ultralytics #AI #ComputerVision #MachineLearning #YOLO #ModelOptimization #CPUInference
Key topics covered:
- Introduction to sparsification: algorithms, techniques, and motivations
- Performance improvements with YOLOv5 sparsification
- Sparse transfer learning and project-based sparsification
- Deploying models with the DeepSparse inference engine
- Exporting models to ONNX format and running with DeepSparse
- Quantization techniques for reducing precision and compute needs
Discover the impressive results of sparsification, including up to 7x faster latency, 12x faster throughput, and 13x smaller file sizes. Mark explains how to apply sparse transfer learning to fine-tune models on custom datasets, keeping architectures efficient while maintaining high accuracy.
Don't miss out on the next steps, including updates to YOLOv5 and YOLOv5 P6 models and upcoming research on knowledge distillation techniques. This video is a must-watch for anyone looking to optimize and deploy YOLOv5 models on CPUs.
🔗 Explore more:
Join our community and stay updated with the latest in AI and computer vision. Like, subscribe, and visit our links for more insights!
#YOLOv5 #Sparsification #DeepSparse #NeuralMagic #Ultralytics #AI #ComputerVision #MachineLearning #YOLO #ModelOptimization #CPUInference
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