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ultralytics 8.0.91 tracker fix and docs comments (#2343)

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Glenn Jocher 2023-05-01 22:20:05 +02:00 • committed by GitHub
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docs/yolov5/tutorials/neural_magic_pruning_quantization.md
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@ -1,3 +1,7 @@
---
comments: true
---
<!--
Copyright (c) 2021 - present / Neuralmagic, Inc. All Rights Reserved.
@ -30,8 +34,6 @@ Put simply, DeepSparse gives you the performance of GPUs and the simplicity of s
- **Infinite Scalability**: Scale vertically to 100s of cores, out with standard Kubernetes, or fully-abstracted with Serverless
- **Easy Integration**: Clean APIs for integrating your model into an application and monitoring it in production
**[Start your 90 day Free Trial](https://neuralmagic.com/deepsparse-free-trial/?utm_campaign=free_trial&utm_source=ultralytics_github).**
### How Does DeepSparse Achieve GPU-Class Performance?
DeepSparse takes advantage of model sparsity to gain its performance speedup.
@ -256,5 +258,3 @@ deepsparse.benchmark zoo:cv/detection/yolov5-s/pytorch/ultralytics/coco/pruned35
## Get Started With DeepSparse
**Research or Testing?** DeepSparse Community is free for research and testing. Get started with our [Documentation](https://docs.neuralmagic.com/).
**Want to Try DeepSparse Enterprise?** [Start your 90 day free trial](https://neuralmagic.com/deepsparse-free-trial/?utm_campaign=free_trial&utm_source=ultralytics_github).