https://github.com/Deci-AI/super-gradients

Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.
https://github.com/Deci-AI/super-gradients

dependency-graph

Last synced: 8 months ago
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Easily train or fine-tune SOTA computer vision models with one open source training library. The home of Yolo-NAS.

• Host: GitHub
• URL: https://github.com/Deci-AI/super-gradients
• Owner: Deci-AI
• License: apache-2.0
• Created: 2021-11-28T07:58:02.000Z (almost 4 years ago)
• Default Branch: master
• Last Pushed: 2024-09-17T11:57:29.000Z (about 1 year ago)
• Last Synced: 2024-10-29T15:05:02.196Z (about 1 year ago)
• Topics: dependency-graph
• Language: Jupyter Notebook
• Homepage: https://www.supergradients.com
• Size: 401 MB
• Stars: 4,572
• Watchers: 45
• Forks: 502
• Open Issues: 114
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE.YOLONAS-POSE.md
  • Codeowners: .github/CODEOWNERS

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• awesome-yolo - **Yolo-NAS** - AI. They used their proprietary Neural Architecture Search (AutoNAC) to find and optimize a new Deep Learning Architecture Yolo-NAS: number and sizes of the stages, blocks, channels. Using quantization-aware “QSP” and “QCI” modules consisting of QA-RepVGG blocks provide 8-bit quantization and ensuring that model architecture would be compatible with Post-Training Quantization (PTQ) - giving minimal accuracy loss during PTQ. Yolo-NAS also use hybrid quantization method that selectively quantizes specific layers to optimize accuracy and latency tradeoffs as well as the attention mechanism and inference time reparametrization to enhance detection capabilities. Pre-trained weights are available for research use (non-commercial) on SuperGradients, Deci’s PyTorch-based, open-source CV library. (Uncategorized / Uncategorized)
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