YOLOX | YOLOv8 | YOLOv7 | YOLOv5 | PIDNet | PyTorch-CIFAR-Models

Use PyNetsPresso for a seamless model optimization process. PyNetsPresso resolves AI-related constraints in business use cases and enables cost-efficiency and enhanced performance by removing the requirement for high-spec servers and network connectivity and preventing high latency and personal data breaches.

Easily compress various models with our resources. Please browse the Docs for details, and join our Discussion Forum for providing feedback or sharing your use cases.

To get started with the NetsPresso Python package, you will need to sign up either at NetsPresso or PyNetsPresso.

Steps	Types	Description
Train (Model Zoo)	Image Classification PyTorch-CIFAR-Models Object Detection YOLOX YOLOv5 YOLOv7 Semantic Segmentation PIDNet Pose Estimation YOLOv8	Build and train models.
Compress	np.compressor	Compress and optimize the user’s pre-trained model.
Convert	np.launcher	Convert AI models to run efficiently on the desired hardware and provide easy installation for seamless usage of the converted AI models.

NetsPresso Model Compressor Best Practice

If you want to experience Model Compressor online without any installation, please refer to the NetsPresso-Model-Compressor-ModelZoo repo that runs on Google Colab.

Contact

Join our Discussion Forum for providing feedback or sharing your use cases, and if you want to talk more with Nota, please contact us here.
Or you can also do it via email([email protected]) or phone(+82 2-555-8659)!